Explainability is key to enhancing the trustworthiness of artificial intelligence in medicine. However, there exists a significant gap between physicians\' expectations for model explainability and the actual behavior of these models. This gap arises from the absence of a consensus on a physician-centered evaluation framework, which is needed to quantitatively assess the practical benefits that effective explainability should offer practitioners. Here, we hypothesize that superior attention maps, as a mechanism of model explanation, should align with the information that physicians focus on, potentially reducing prediction uncertainty and increasing model reliability. We employed a multimodal transformer to predict lymph node metastasis of rectal cancer using clinical data and magnetic resonance imaging. We explored how well attention maps, visualized through a state-of-the-art technique, can achieve agreement with physician understanding. Subsequently, we compared two distinct approaches for estimating uncertainty: a standalone estimation using only the variance of prediction probability, and a human-in-the-loop estimation that considers both the variance of prediction probability and the quantified agreement. Our findings revealed no significant advantage of the human-in-the-loop approach over the standalone one. In conclusion, this case study did not confirm the anticipated benefit of the explanation in enhancing model reliability. Superficial explanations could do more harm than good by misleading physicians into relying on uncertain predictions, suggesting that the current state of attention mechanisms should not be overestimated in the context of model explainability.

The main aim of this research is to present an innovative method known as fuzzy network data envelopment analysis (FNDEA) in order to assess the performance of network decision-making units (DMUs) that possess a two-stage structure while taking into account the uncertainty of data. To attain this goal, we utilize various methodologies including the non-cooperative game (leader-follower) NDEA method, the concept of Z-number, credibility theory, and chance-constrained programming (CCP) to develop a model for the fuzzy NDEA approach. The FNDEA approach offers several advantages, such as the linearity of the presented FNDEA models, the ability to rank two-stage DMUs in situations of ambiguity, the provision of a unique efficiency decomposition method in an uncertain environment, and the capability to handle Z-information. To demonstrate the applicability and effectiveness of the proposed approach, we implement the Z-number network data envelopment analysis (ZNDEA) approach in assessing the performance of Iranian private insurance companies. The results of this implementation reveal that the proposed ZNDEA method is suitable and effective for measuring and ranking insurance companies in situations where data ambiguity is present.

BACKGROUND: Pancreatic cystic neoplasms (PCN) are considered premalignant conditions to pancreatic adenocarcinoma with varying degrees of cancerous potential. Management for individuals who do not require surgical treatment involves surveillance to assess for cancerous progression. Little is known about patients\' experience and the impact of living with surveillance for these lesions.
OBJECTIVE: To explore the experiences of patients living with surveillance for PCNs.
METHODS: Semi-structured qualitative interviews were conducted with patients under surveillance for pancreatic cystic neoplasms in the UK. Age, gender, time from surveillance and surveillance method were used to purposively sample the patient group. Data were analysed using reflexive thematic analysis.
RESULTS: A PCN diagnosis is incidental and unexpected and for some, the beginning of a disruptive experience. How patients make sense of their PCN diagnosis is influenced by their existing understanding of pancreatic cancer, explanations from clinicians and the presence of coexisting health concerns. A lack of understanding of the diagnosis and its meaning for their future led to an overarching theme of uncertainty for the PCN population. Surveillance for PCN could be seen as a reminder of fears of PCN and cancer, or as an opportunity for reassurance.
CONCLUSIONS: Currently, individuals living with surveillance for PCNs experience uncertainty with a lack of support in making sense of a prognostically uncertain diagnosis with no immediate treatment. More research is needed to identify the needs of this population to make improvements to patient care and reduce negative experiences.

BACKGROUND: A new quality assurance and control method for electrometers using a new current source, different from the method published in the guidelines for electrometers, has been reported. This current source uses dry batteries and exhibits excellent performance in terms of voltage, temperature, and time characteristics. The electrometer sensitivity coefficient can be calculated by comparing the sensitivity of one electrometer with that of another on the electrometer calibration coefficient that has been calibrated by a calibration laboratory in advance in both methods. The guideline method requires two or more sets of ionization chambers and electrometers in the facility. In contrast, our method does not use ionization chambers; therefore, the sensitivity ratio of the electrometer can be measured in any facility. This study compared the uncertainty of the electrometer sensitivity factor calculated using the new current source method (current method) with that calculated using a linear accelerator (LINAC) and ionization chambers (LINAC method) described in the electrometer guidelines.
METHODS: In this study, we used a current source that we invented previously by Kawaguchi Electric Works in Japan. The sensitivity ratios of the electrometers were measured with three manufacture\'s electrometers. The electrometer sensitivity factor was calculated by multiplying the electrometer calibration coefficient. The ionization chamber was 30013 (PTW), and the current source was the current obtained from 10 MV TrueBeam X-rays under calibration conditions. The mean value, standard deviation, and coefficient of variation were calculated. The time required to set up the ionization chamber for calculating the sensitivity ratio of the electrometer was also measured. The accuracy was confirmed by calculating the expanded uncertainty of the electrometer sensitivity coefficients.
RESULTS: The LINAC method had a maximum coefficient of variation of 0.072%. The gross time of the LINAC method was approximately 110 min. The current method had a maximum coefficient of variation of 0.0055% and took less than half the time taken by the LINAC method (35 min) because there was no waiting time for the ionization chamber to be set up and the applied voltage to stabilize under calibration conditions. The expanded uncertainties of the electrometer calibration coefficients were 0.36% and 0.36%, respectively.
CONCLUSIONS: The new cross-comparison method for electrometer sensitivity factors using a current source is more efficient and useful than the linear accelerator method described in the guidelines; furthermore, this method ensured accuracy for quality assurance and control of electrometers.

Objective.High-dose-rate (HDR) brachytherapy lacks routinely available treatment verification methods. Real-time tracking of the radiation source during HDR brachytherapy can enhance treatment verification capabilities. Recent developments in source tracking allow for measurement of dwell times and source positions with high accuracy. However, more clinically relevant information, such as dose discrepancies, is still needed. To address this, a real-time dose calculation implementation was developed to provide more relevant information from source tracking data. A proof-of-principle of the developed tool was shown using source tracking data obtained from a 3D-printed anthropomorphic phantom.Approach.Software was developed to calculate dose-volume-histograms (DVH) and clinical dose metrics from experimental HDR prostate treatment source tracking data, measured in a realistic pelvic phantom. Uncertainty estimation was performed using repeat measurements to assess the inherent dose measuring uncertainty of thein vivodosimetry (IVD) system. Using a novel approach, the measurement uncertainty can be incorporated in the dose calculation, and used for evaluation of cumulative dose and clinical dose-volume metrics after every dwell position, enabling real-time treatment verification.Main results.The dose calculated from source tracking measurements aligned with the generated uncertainty bands, validating the approach. Simulated shifts of 3 mm in 5/17 needles in a single plan caused DVH deviations beyond the uncertainty bands, indicating errors occurred during treatment. Clinical dose-volume metrics could be monitored in a time-resolved approach, enabling early detection of treatment plan deviations and prediction of their impact on the final dose that will be delivered in real-time.Significance.Integrating dose calculation with source tracking enhances the clinical relevance of IVD methods. Phantom measurements show that the developed tool aids in tracking treatment progress, detecting errors in real-time and post-treatment evaluation. In addition, it could be used to define patient-specific action limits and error thresholds, while taking the uncertainty of the measurement system into consideration.

Suitable combinations of observed datasets for estimating crop model parameters can reduce the computational cost while ensuring accuracy. This study aims to explore the quantitative influence of different combinations of the observed phenological stages on estimation of cultivar-specific parameters (CPSs). We used the CROPGRO-Soybean phenological model (CSPM) as a case study in combination with the Generalized Likelihood Uncertainty Estimation (GLUE) method. Different combinations of four observed phenological stages, including initial flowering, initial pod, initial grain, and initial maturity stages for five soybean cultivars from Exp. 1 and Exp. 3 described in Table 2 are respectively used to calibrate the CSPs. The CSPM, driven by the optimized CSPs, is then evaluated against two independent phenological datasets from Exp. 2 and Exp. 4 described in Table 2. Root means square error (RMSE) (mean absolute error (MAE), coefficient of determination (R2), and Nash Sutcliffe model efficiency (NSE)) are 15.50 (14.63, 0.96, 0.42), 4.76 (3.92, 0.97, 0.95), 4.69 (3.72, 0.98, 0.95), 3.91 (3.40, 0.99, 0.96) and 12.54 (11.67, 0.95, 0.60), 5.07 (4.61, 0.98, 0.93), 4.97 (4.28, 0.97, 0.94), 4.58 (4.02, 0.98, 0.95) for using one, two, three, and four observed phenological stages in the CSPs estimation. The evaluation results suggest that RMSE and MAE decrease, and R2 and NSE increase with the increase in the number of observed phenological stages used for parameter calibration. However, there is no significant reduction in the RMSEs (MAEs, NSEs) using two, three, and four observed stages. Relatively reliable optimized CSPs for CSMP are obtained by using at least two observed phenological stages balancing calibration effect and computational cost. These findings provide new insight into parameter estimation of crop models.

OBJECTIVE: This study aims to investigate the Readiness for Return-to-Work (RRTW) of patients with head and neck tumours and to analyse the relationships among self-efficacy, disease uncertainty, psychosocial adaptation, and RRTW in head and neck cancer (HNC) patients.
METHODS: A cross-sectional study was conducted with 259 HNC patients with a discharge length of ≥1 month at a tertiary hospital in Liaoning Province. The research tools included a self-designed general information questionnaire, the Readiness for Return-to-Work (RRTW) Scale, the General Self-Efficacy Scale (GSES), the Mishel Uncertainty in Illness Scale (MUIS), and the Self-Reporting Psychosocial Adjustment to Illness Scale (PAIS-SR). Descriptive statistical analysis, the rank sum test, Spearman correlation analysis, and ordered multiple and dichotomous logistic regression analyses were used.
RESULTS: The overall RRTW among HNC patients was low (41.9%). HNC patients who did not return to work were mainly in the precontemplation stage (38.1%) and contemplation stage (29.9%). HNC patients who returned to work were mainly in the active maintenance stage (64.2%). Children\'s status (OR = 0.218, 95% CI 0.068-0.703), self-efficacy (OR = 1.213, 95% CI 1.012-1.454), unpredictability (OR = 0.845, 95% CI 0.720-0.990), occupational environment (OR = 0.787, 95% CI 0.625-0.990), and family environment (OR = 0.798, 95% CI 0.643-0.990) influence the RRTW of HNC patients who have not returned to work. Educational level (OR = 62.196, 95% CI 63.307-68.567), children\'s status (OR = 0.058, 95% CI 1.004-2.547), self-efficacy (OR = 1.544, 95% CI 3.010-8.715), unpredictability (OR = 0.445, 95% CI 1.271-2.280), and psychological status (OR = 0.340, 95% CI 1.141-2.401) influence the RRTW of HNC patients who have returned to work.
CONCLUSIONS: Children\'s status, education level, self-efficacy, illness uncertainty, and psychosocial adjustment are crucial to RRTW. This study provides a theoretical basis for formulating intervention measures aimed at improving the RRTW of patients.

BACKGROUND: Diagnostic uncertainty is common, but its communication to patients is under-explored. This study aimed to (1) characterise variation in doctors\' communication of diagnostic uncertainty and (2) explore why variation occurred.
METHODS: Four written vignettes of clinical scenarios involving diagnostic uncertainty were developed. Doctors were recruited from five hospitals until theoretical saturation was reached (n = 36). Participants read vignettes in a randomised order, and were asked to discuss the diagnosis/plan with an online interviewer, as they would with a \'typical patient\'. Semi-structured interviews explored reasons for communication choices. Interview transcripts were coded; quantitative and qualitative (thematic) analyses were undertaken.
RESULTS: There was marked variation in doctors\' communication: in their discussion about differential diagnoses, their reference to the level of uncertainty in diagnoses/investigations and their acknowledgement of diagnostic uncertainty when safety-netting. Implicit expressions of uncertainty were more common than explicit. Participants expressed both different communication goals (including reducing patient anxiety, building trust, empowering patients and protecting against diagnostic errors) and different perspectives on how to achieve these goals. Training in diagnostic uncertainty communication is rare, but many felt it would be useful.
CONCLUSIONS: Significant variation in diagnostic uncertainty communication exists, even in a controlled setting. Differing communication goals-often grounded in conflicting ethical principles, for example, respect for autonomy versus nonmaleficence-and differing ideas on how to prioritise and achieve them may underlie this. The variation in communication behaviours observed has important implications for patient safety and health inequalities. Patient-focused research is required to guide practice.
UNASSIGNED: In the design stage of the study, two patient and public involvement groups (consisting of members of the public of a range of ages and backgrounds) were consulted to gain an understanding of patient perspectives on the concept of communicating diagnostic uncertainty. Their feedback informed the formulations of the research questions and the choice of vignettes used.

The mother is traditionally the primary caregiver for the child, and uncertainty is a major source of psychological distress for parents of sick children. As a result, the current study sought to investigate the impact of an educational program on illness uncertainty in mothers of children with insulin-dependent diabetes mellitus (IDDM). This is a quasi-experimental study with a pretest-posttest design, involving 40 mothers of children with IDDM. After visiting the research site and recruiting eligible mothers, the researcher used the purposive sampling method to randomly assign them to one of two intervention and control groups. Taking into account their needs and conditions, mothers were scheduled for at least three sessions, with a 30-minute interval every other day. The data analysis revealed no significant differences between the two groups in terms of child age and gender, mother age and education level, number of children, and duration of diabetes. There was a significant difference in the total score of uncertainty, total score of ambiguity, lack of clarity, lack of information, unpredictability, and their dimensions between mothers of diabetic children before and after the intervention (P=0.02). Based on the findings, it is recommended that the educational needs of children and their mothers be assessed upon admission and that educational content be prepared and taught accordingly, thereby helping to reduce illness uncertainty in mothers.

【Purpose】 Accurate control of X-ray units and dosimeters and analysis of the uncertainties associated with the accurate measurement of radiation doses are essential for the effective establishment and application of diagnostic reference levels. In this study, the uncertainty of the average glandular dose (AGD) in the quality control of mammography equipment was evaluated in detail, and recommendations were provided to improve the accuracy and safety of radiological practice. 【Methods】 In the uncertainty analysis of the AGD, the relative standard uncertainties in the measurements of the half-value layer, the incident air kerma, and the conversion factor were considered and finally expressed as expanded uncertainties, the intervals of which were clearly defined. 【Results】 From the AGD measurements using two types of dosimeters, it was found that the primary sources of uncertainty are the uncertainty of the calibration factors of the dosimeters and the uncertainty of the conversion factors.【Conclusion】 To reduce uncertainty, the use of regularly calibrated dosimeters is effective and reliable. Two types of dosimeters are commonly used; the results of this study may serve as a reference value for the uncertainty of AGD in quality control in medical facilities.