OBJECTIVE: Peritonsillar abscess (PTA) is the most common deep soft tissue infection necessitating surgical intervention in the head and neck region. Potential causes include infections of the palatine tonsils, palatine glands, or branchiogenic rudiments (\"acute tonsillitis hypothesis\" vs. \"Weber\'s gland hypothesis\"). Understanding the currently still unknown predominant cause is crucial for guiding therapeutic strategies, such as abscess tonsillectomy versus incision and drainage alone. This study aims to investigate the pre-diagnoses associated with subsequent PTA using a nationally representative practice database in Germany.
METHODS: Data were collected from 195 ENT practices across Germany utilizing the nationally representative practice database IQVIA™ Disease Analyzer. Included were patients aged 18 years and older with a first diagnosis of PTA (index date) between January 2005 and December 2022 and a minimum observation period of 12 months preceding the index date. These patients were matched (1:5) with controls without PTA, based on age, sex, and index year. Frequencies of prior diagnoses coded according to ICD-10 in the 12 months preceding the index date were computed. The association between prior diagnoses and PTA was evaluated using multivariable logistic regression (MLR) and sensitivity analysis (SA).
RESULTS: A total of 5,325 cases were compared with 26,725 controls in the multivariable logistic regression (MLR) analysis, and 16,251 cases were compared with 81,255 controls in the sensitivity analysis (SA). Mean age was 45.3 ± 18.3 years (MLR) and 41.9 ± 16.7 years (SA). The proportion of female patients was 51.8% (MLR) and 46.9% (SA), respectively. MLR showed the strongest associations with PTA for the prior diagnoses of \"acute tonsillitis\" (odds ratio, OR: 6.71; 95% CI: 5.81-7.74), \"chronic tonsillitis\" (OR: 2.00; 95% CI: 1.58-2.52), and \"acute pharyngitis\" (OR: 1.74; 95% CI: 1.50-2.03). SA similarly indicated the strongest associations with PTA for the prior diagnoses of \"acute tonsillitis\" (OR: 5.02; 95% CI: 4.60-5.47), \"chronic tonsillitis\" (OR: 1.87; 95% CI: 1.64-2.12), and \"acute pharyngitis\" (OR: 1.27; 95% CI: 1.14-1.41).
CONCLUSIONS: The most prevalent prior diagnosis associated with PTA was acute tonsillitis, followed by chronic tonsillitis and acute pharyngitis. The association with acute pharyngitis suggests possible non-tonsillogenic causes. Other specific causes of PTA, such as inflammation of the palatine gland or branchiogenic remnants, are not captured by the ICD system or the database utilized in this study.

UNASSIGNED: Natural language processing (NLP) can generate diagnoses codes from imaging reports. Meanwhile, the International Classification of Diseases (ICD-10) codes are the United States\' standard for billing/coding, which enable tracking disease burden and outcomes. This cross-sectional study aimed to test feasibility of an NLP algorithm\'s performance and comparison to radiologists\' and physicians\' manual coding.
UNASSIGNED: Three neuroradiologists and one non-radiologist physician reviewers manually coded a randomly-selected pool of 200 craniospinal CT and MRI reports from a pool of >10,000. The NLP algorithm (Radnosis, VEEV, Inc., Minneapolis, MN) subdivided each report\'s Impression into \"phrases\", with multiple ICD-10 matches for each phrase. Only viewing the Impression, the physician reviewers selected the single best ICD-10 code for each phrase. Codes selected by the physicians and algorithm were compared for agreement.
UNASSIGNED: The algorithm extracted the reports\' Impressions into 645 phrases, each having ranked ICD-10 matches. Regarding the reviewers\' selected codes, pairwise agreement was unreliable (Krippendorff α = 0.39-0.63). Using unanimous reviewer agreement as \"ground truth\", the algorithm\'s sensitivity/specificity/F2 for top 5 codes was 0.88/0.80/0.83, and for the single best code was 0.67/0.82/0.67. The engine tabulated \"pertinent negatives\" as negative codes for stated findings (e.g. \"no intracranial hemorrhage\"). The engine\'s matching was more specific for shorter than full-length ICD-10 codes (p = 0.00582x10-3).
UNASSIGNED: Manual coding by physician reviewers has significant variability and is time-consuming, while the NLP algorithm\'s top 5 diagnosis codes are relatively accurate. This preliminary work demonstrates the feasibility and potential for generating codes with reliability and consistency. Future works may include correlating diagnosis codes with clinical encounter codes to evaluate imaging\'s impact on, and relevance to care.

Mental disorders characterized by preoccupation with distressing bodily symptoms and associated functional impairment have been a target of major reconceptualization in the ICD-11, in which a single category of Bodily Distress Disorder (BDD) with different levels of severity replaces most of the Somatoform Disorders in ICD-10. This study compared the accuracy of clinicians\' diagnosis of disorders of somatic symptoms using either the ICD-11 or ICD-10 diagnostic guidelines in an online study.
Clinically active members of the World Health Organization\'s Global Clinical Practice Network (N = 1065) participating in English, Spanish, or Japanese were randomly assigned to apply ICD-11 or ICD-10 diagnostic guidelines to one of nine pairs of standardized case vignettes. The accuracy of the clinicians\' diagnoses as well as their ratings of the guidelines\' clinical utility were assessed.
Overall, clinicians were more accurate using ICD-11 compared to ICD-10 for every presentation of a vignette characterized primarily by bodily symptoms associated with distress and impairment. Clinicians who made a diagnosis of BDD using ICD-11 were generally correct in applying the severity specifiers for the condition.
This sample may represent some self-selection bias and thus may not generalize to all clinicians. Additionally, diagnostic decisions with live patients may lead to different results.
The ICD-11 diagnostic guidelines for BDD represent an improvement over those for Somatoform Disorders in ICD-10 in regard to clinicians\' diagnostic accuracy and perceived clinical utility.

This paper presents a new corpus of radiology medical reports written in Spanish and labeled with ICD-10. CARES (Corpus of Anonymised Radiological Evidences in Spanish) is a high-quality corpus manually labeled and reviewed by radiologists that is freely available for the research community on HuggingFace. These types of resources are essential for developing automatic text classification tools as they are necessary for training and tuning computational systems. However, in the medical domain these are very difficult to obtain for different reasons including privacy and data protection issues or the involvement of medical specialists in the generation of these resources. We present a corpus labeled and reviewed by radiologists in their daily practice that is available for research purposes. In addition, after describing the corpus and explaining how it has been generated, a first experimental approach is carried out using several machine learning algorithms based on transformer language models such as BioBERT and RoBERTa to test the validity of this linguistic resource. The best performing classifier achieved 0.8676 micro and 0.8328 macro f1-score and these results encourage us to continue working in this research line.

BACKGROUND: Comprehensive identification of out-of-hospital cardiac arrest (OHCA) cases for inclusion in registries remains challenging due to the inherent diversity of OHCA aetiology, presentation, and management. The Northern Adelaide Local Health Network (NALHN) OHCA registry identifies OHCAs presenting to NALHN hospitals using existing data sources to monitor in-hospital treatment and survival. This study aimed to investigate the accuracy of hospital-based data sources for identifying OHCA cases treated at hospital.
METHODS: Retrospective analysis of all OHCAs aged >18 years included in the NALHN OHCA registry between 2011-16. Registry cases are identified from an emergency medical service (EMS) OHCA registry, Emergency Department (ED) and ICD-10 coding datasets, and key-word searches of two in-hospital clinical registries. Sensitivity and positive predictive values (PPV) of each hospital-based data source were analysed with respect to (a) the number of cases expected to be identified by that source, (b) total OHCA. Non-OHCAs yielded by each source were explored and a sub-analysis of ICD-10 codes was performed.
RESULTS: Between 2011-16, the four hospital-based sources yielded 992 cases, of which 383 were confirmed as OHCA. The ED coding dataset was the most accurate with a sensitivity and PPV of 78%. The ICD-10 coding dataset had good sensitivity but low PPV (33%). The ED coding dataset, combined with the two in-hospital clinical registries, identified 93% of OHCAs.
CONCLUSIONS: No single dataset identified all OHCAs presenting to NALHN hospitals. Combined hospital-based data sources provide a valid method of identifying OHCAs treated at hospital that may be adapted to augment EMS-based data.

Mental disorders show varying degrees of continuity from childhood to adulthood. This study addresses the relationship of child and adolescent mental disorders to early adult psychiatric morbidity.
From a population at risk of 830,819 children and adolescents aged 6-16 years, we selected all those (n=6043) who were enrolled for the first time in the Danish Psychiatric Register with an ICD-10 F00-99 diagnosis in 1995-1997, and identified any mental disorder for which they received treatment up to 2009.
Neurodevelopmental and conduct disorders were the principal diagnostic groups at 6-16 years and exhibited a characteristic male preponderance; while affective, eating, neurotic, stress-related and adjustment disorders were more common in girls. Over a mean follow-up period of 10.1 years, 1666 (27.6%) cases, mean age 23.4 years, were referred for treatment to mental health services, and they had a markedly higher risk than the general population (RR 5.1; 95% CI 4.9-5.4). Affective, eating, neurodevelopmental, obsessive-compulsive and psychotic disorders had the strongest continuity. Heterotypic transitions were observed for affective, eating, neurodevelopmental, personality and substance use disorders.
These findings suggest that individuals with psychiatric antecedents in childhood and adolescence had a high risk of being referred for treatment in early adulthood, and many mental disorders for which they required treatment revealed both homotypic and heterotypic continuity.

BACKGROUND: In single-payer health care financing systems data extracted from hospital report forms submitted for reimbursement purposes may be used for epidemiological investigations.
OBJECTIVE: Based on data submitted by 14 neurological wards in Central Hungary the authors examined the reliability of these reports.
METHODS: Analyses were performed for the 3-digit codes of the 10th version of the International Classification of Diseases for cerebral infarcts (ICD-10 I63+I64) reported for the National Health Insurance Fund.
RESULTS: The number of cases in individual hospitals changed between a decrease by 35% and an increase by 73% from the first to the second half of the year 2012, reflecting changes in the size of the catchment area of the hospitals in July 2012. Of those with an ICD-10 I63 or I64 discharge diagnosis 54-84% had acute stroke. Neurological wards cared for 34-98% of all stroke patients. The diagnoses submitted for reimbursement purposes corresponded in over 99% to the diagnoses in the hospital discharge reports. Inaccuracies occurred in a larger proportion (about 20%) in coding the DRG financing categories.
CONCLUSIONS: Databases created from hospital reports submitted for reimbursement purposes can be used reliably in Hungary for stroke epidemiological studies.