Scleral lenses (SLs) are large-diameter rigid contact lenses that are a mainstay treatment for eyes with corneal irregularities. In recent years, there has been increased interest in the role of managing dry eye disease (DED) with SLs, as many patients with DED have reported symptomatic relief with SL wear. The role of SLs for DED management when there are associated corneal irregularities is supported by individual case reports and studies. This has prompted practitioners to begin advocating using SLs in DED cases, even in the absence of associated corneal irregularities and other ocular surface diseases (OSDs). There have also been discussions on potentially placing SLs earlier in the treatment hierarchy of DED, where it currently sits at a more advanced level of intervention (Step 3) in the TFOS DEWS II Report. This review will present the currently available, albeit sparse, evidence that supports and suggests this practice, as well as ancillary evidence supporting the purported benefits of SL wear in DED. The advantages of SL wear, such as corneal healing, absence of tear evaporation and contact lens dehydration, and improved visual acuity with associated increased wear comfort, and how this will benefit DED patients will be explored. Conversely, the challenges associated with fitting SLs in DED patients, including increased midday fogging, poor wettability, and subjective patient satisfaction, will also be presented, as well as a discussion on the key considerations for SL fitting in this population. Overall, while more research is needed to support the use of SLs in DED patients without associated corneal irregularities and other forms of OSD, the use of these lenses may prove to have a potentially wider role given their reported ancillary benefits in these populations.

OBJECTIVE: Evaluate the level of information of pediatricians about the diagnosis and management of cryptorchidism.
METHODS: A cross-sectional observational study was conducted using a form via the \"Google Forms\" platform. The study population included pediatricians and pediatric residents associated with the Brazilian Society of Pediatrics. Seven hundred twenty-eight responses were recorded and analyzed using IBM SPSS v21.
RESULTS: 728 valid responses were obtained. Of these answers, only 20.5 % answered that the physical examination was sufficient for the diagnosis, and 79.4 % responded that they requested ultrasound as the best test to aid in diagnosing cryptorchidism. When questioned about the ideal age for referring a patient with cryptorchidism, the survey recorded 56.3 % of the responses defending the correct age as six months old, 30.2 % shortly after birth, and 13.2 % at two years old. Other topics were addressed in the form, such as the frequency of evaluation of testicular position and investigation for DDS, among others. Still, the answers to these questions were compatible with current manuals and guidelines on cryptorchidism.
CONCLUSIONS: It is evident that the understanding of the professionals consulted about the diagnosis and management of cryptorchidism needs to be updated with the current practices adopted and that pediatricians, in general, must maintain periodic programs on this subject. Therefore, this topic should be part of a continuing education program with pediatric surgery.

Lateral plating alone has been postulated as an alternative for fixation of bicondylar tibial plateau fractures in attempts to limit morbidity associated with dual plating. Characterization of fracture patterns that may facilitate lateral plating alone for bicondylar tibial plateau fractures is not well established. The authors analyzed radiographic and clinical outcomes of isolated lateral plating in patients with at least 6 months of follow-up. Of 56 patients identified, 37 (66%) had 41 AO Foundation (AO)/Orthopaedic Trauma Association (OTA) C1/C2 fractures with 19 (34%) presenting with 41 C3 fractures. Mean posteromedial articular fracture angle (PMAFA) was 69.9 degrees, with an average of 1.3 medial articular fragments. Only 16 patients (28%) had a PMAFA under 45 degrees. There were no cases of nonunion, and five patients (8.9%) developed wound infection during follow-up. Four patients (7.1%) experienced malreduction over three degrees, and eight patients (14.3%) experienced change in alignment over the follow-up duration, indicating some risk of inadequate fixation with this technique. (Journal of Surgical Orthopaedic Advances 33(2):088-092, 2024).

OBJECTIVE: Home treatment is considered safe in acute pulmonary embolism (PE) patients selected by a validated triage tool (e.g. simplified PE severity index score or Hestia rule), but there is uncertainty regarding the applicability in underrepresented subgroups. The aim was to evaluate the safety of home treatment by performing an individual patient-level data meta-analysis.
METHODS: Ten prospective cohort studies or randomized controlled trials were identified in a systematic search, totalling 2694 PE patients treated at home (discharged within 24 h) and identified by a predefined triage tool. The 14- and 30-day incidences of all-cause mortality and adverse events (combined endpoint of recurrent venous thromboembolism, major bleeding, and/or all-cause mortality) were evaluated. The relative risk (RR) for 14- and 30-day mortalities and adverse events is calculated in subgroups using a random effects model.
RESULTS: The 14- and 30-day mortalities were 0.11% [95% confidence interval (CI) 0.0-0.24, I2 = 0) and 0.30% (95% CI 0.09-0.51, I2 = 0). The 14- and 30-day incidences of adverse events were 0.56% (95% CI 0.28-0.84, I2 = 0) and 1.2% (95% CI 0.79-1.6, I2 = 0). Cancer was associated with increased 30-day mortality [RR 4.9; 95% prediction interval (PI) 2.7-9.1; I2 = 0]. Pre-existing cardiopulmonary disease, abnormal troponin, and abnormal (N-terminal pro-)B-type natriuretic peptide [(NT-pro)BNP] at presentation were associated with an increased incidence of 14-day adverse events [RR 3.5 (95% PI 1.5-7.9, I2 = 0), 2.5 (95% PI 1.3-4.9, I2 = 0), and 3.9 (95% PI 1.6-9.8, I2 = 0), respectively], but not mortality. At 30 days, cancer, abnormal troponin, and abnormal (NT-pro)BNP were associated with an increased incidence of adverse events [RR 2.7 (95% PI 1.4-5.2, I2 = 0), 2.9 (95% PI 1.5-5.7, I2 = 0), and 3.3 (95% PI 1.6-7.1, I2 = 0), respectively].
CONCLUSIONS: The incidence of adverse events in home-treated PE patients, selected by a validated triage tool, was very low. Patients with cancer had a three- to five-fold higher incidence of adverse events and death. Patients with increased troponin or (NT-pro)BNP had a three-fold higher risk of adverse events, driven by recurrent venous thromboembolism and bleeding.

Virtual patients (VPs) have long been used to teach and assess clinical reasoning. VPs can be programmed to simulate authentic patient-clinician interactions and to reflect a variety of contextual permutations. However, their use has historically been limited by the high cost and logistical challenges of large-scale implementation. We describe a novel globally-accessible approach to develop low-cost VPs at scale using artificial intelligence (AI) large language models (LLMs). We leveraged OpenAI Generative Pretrained Transformer (GPT) to create and implement two interactive VPs, and created permutations that differed in contextual features. We used systematic prompt engineering to refine a prompt instructing ChatGPT to emulate the patient for a given case scenario, and then provide feedback on clinician performance. We implemented the prompts using GPT-3.5-turbo and GPT-4.0, and created a simple text-only interface using the OpenAI API. GPT-4.0 was far superior. We also conducted limited testing using another LLM (Anthropic Claude), with promising results. We provide the final prompt, case scenarios, and Python code. LLM-VPs represent a \'disruptive innovation\' - an innovation that is unmistakably inferior to existing products but substantially more accessible (due to low cost, global reach, or ease of implementation) and thereby able to reach a previously underserved market. LLM-VPs will lay the foundation for global democratization via low-cost-low-risk scalable development of educational and clinical simulations. These powerful tools could revolutionize the teaching, assessment, and research of management reasoning, shared decision-making, and AI evaluation (e.g. \'software as a medical device\' evaluations).

OBJECTIVE: In a time of exponential growth of new evidence supporting clinical decision-making, combined with a labor-intensive process of selecting this evidence, methods are needed to speed up current processes to keep medical guidelines up-to-date. This study evaluated the performance and feasibility of active learning to support the selection of relevant publications within medical guideline development and to study the role of noisy labels.
METHODS: We used a mixed-methods design. Two independent clinicians\' manual process of literature selection was evaluated for 14 searches. This was followed by a series of simulations investigating the performance of random reading versus using screening prioritization based on active learning. We identified hard-to-find papers and checked the labels in a reflective dialogue.
METHODS: Inter-rater reliability was assessed using Cohen\'s Kappa (ĸ). To evaluate the performance of active learning, we used the Work Saved over Sampling at 95% recall (WSS@95) and percentage Relevant Records Found at reading only 10% of the total number of records (RRF@10). We used the average time to discovery (ATD) to detect records with potentially noisy labels. Finally, the accuracy of labeling was discussed in a reflective dialogue with guideline developers.
RESULTS: Mean ĸ for manual title-abstract selection by clinicians was 0.50 and varied between - 0.01 and 0.87 based on 5.021 abstracts. WSS@95 ranged from 50.15% (SD = 17.7) based on selection by clinicians to 69.24% (SD = 11.5) based on the selection by research methodologist up to 75.76% (SD = 12.2) based on the final full-text inclusion. A similar pattern was seen for RRF@10, ranging from 48.31% (SD = 23.3) to 62.8% (SD = 21.20) and 65.58% (SD = 23.25). The performance of active learning deteriorates with higher noise. Compared with the final full-text selection, the selection made by clinicians or research methodologists deteriorated WSS@95 by 25.61% and 6.25%, respectively.
CONCLUSIONS: While active machine learning tools can accelerate the process of literature screening within guideline development, they can only work as well as the input given by human raters. Noisy labels make noisy machine learning.

» Artificial intelligence is an umbrella term for computational calculations that are designed to mimic human intelligence and problem-solving capabilities, although in the future, this may become an incomplete definition. Machine learning (ML) encompasses the development of algorithms or predictive models that generate outputs without explicit instructions, assisting in clinical predictions based on large data sets. Deep learning is a subset of ML that utilizes layers of networks that use various inter-relational connections to define and generalize data.» ML algorithms can enhance radiomics techniques for improved image evaluation and diagnosis. While ML shows promise with the advent of radiomics, there are still obstacles to overcome.» Several calculators leveraging ML algorithms have been developed to predict survival in primary sarcomas and metastatic bone disease utilizing patient-specific data. While these models often report exceptionally accurate performance, it is crucial to evaluate their robustness using standardized guidelines.» While increased computing power suggests continuous improvement of ML algorithms, these advancements must be balanced against challenges such as diversifying data, addressing ethical concerns, and enhancing model interpretability.

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OBJECTIVE: To assess the clinical performance of ProsTAV®, a blood-based test based on telomere associate variables (TAV) measurement, to support biopsy decision-making when diagnosing suspicious prostate cancer (PCa).
METHODS: Preliminary data of a prospective observational pragmatic study of patients with prostate-specific antigen (PSA) levels 3-10 ng/ml and suspicious PCa. Results were combined with other clinical data, and all patients underwent prostate biopsies according to each center\'s routine clinical practice, while magnetic resonance imaging (MRI) before the prostate biopsy was optional. Sensitivity, specificity, positive and negative predicted values, and subjects where biopsies could have been avoided using ProsTAV were determined.
RESULTS: The mean age of the participants (n = 251) was 67.4 years, with a mean PSA of 5.90 ng/ml, a mean free PSA of 18.9%, and a PSA density of 0.14 ng/ml. Digital rectal examination was abnormal in 21.1% of the subjects, and according to biopsy, the prevalence of significant PCa was 47.8%. The area under the ROC curve of ProsTAV was 0.7, with a sensitivity of 0.90 (95% CI, 0.85-0.95) and specificity of 0.27 (95% CI, 0.19-0.34). The positive and negative predictive values were 0.53 (95% CI, 0.46-0.60) and 0.74 (95% CI, 0.62-0.87), respectively. ProsTAV could have reduced the biopsies performed by 27% and showed some initial evidence of a putative benefit in the diagnosis pathway combined with MRI.
CONCLUSIONS: ProsTAV increases the prediction capacity of significant PCa in patients with PSA between 3 and 10 ng/ml and could be considered a complementary tool to improve the patient diagnosis pathway.

UNASSIGNED: Artificial intelligence tools such as Chat Generative Pre-trained Transformer (ChatGPT) have been used for many health care-related applications; however, there is a lack of research on their capabilities for evaluating morally and/or ethically complex medical decisions. The objective of this study was to assess the moral competence of ChatGPT.
UNASSIGNED: This cross-sectional study was performed between May 2023 and July 2023 using scenarios from the Moral Competence Test (MCT). Numerical responses were collected from ChatGPT 3.5 and 4.0 to assess individual and overall stage scores, including C-index and overall moral stage preference. Descriptive analysis and 2-sided Student\'s t-test were used for all continuous data.
UNASSIGNED: A total of 100 iterations of the MCT were performed and moral preference was found to be higher in the latter Kohlberg-derived arguments. ChatGPT 4.0 was found to have a higher overall moral stage preference (2.325 versus 1.755) when compared to ChatGPT 3.5. ChatGPT 4.0 was also found to have a statistically higher C-index score in comparison to ChatGPT 3.5 (29.03 ± 11.10 versus 19.32 ± 10.95, P =.0000275).
UNASSIGNED: ChatGPT 3.5 and 4.0 trended towards higher moral preference for the latter stages of Kohlberg\'s theory for both dilemmas with C-indices suggesting medium moral competence. However, both models showed moderate variation in C-index scores indicating inconsistency and further training is recommended.
UNASSIGNED: ChatGPT demonstrates medium moral competence and can evaluate arguments based on Kohlberg\'s theory of moral development. These findings suggest that future revisions of ChatGPT and other large language models could assist physicians in the decision-making process when encountering complex ethical scenarios.