Severe Vocal Cord Dysfunction: An Unusual Complication of Juvenile Dermatomyositis
Bibliographic record
Abstract
Aim Sudden cardiac death is the leading medical cause of death during exercise.1 Our objective was to retrospectively analyse the routine cardiac assessment of professional footballers to aid physician management and improve player safety. Methods Footballers from five professional clubs between March 2012 and October 2014 were included (n=265). All were performed in line with the recommendations of the Football Association Cardiology Committee, incorporating clinical examination, 12-lead ECG, echocardiography and health questionnaire.2 Data was retrospectively collected, inspected and analysed using Excel spreadsheets. Findings were classified as ‘normal’ or ‘not normal’, and not normal assessments were further broken down into ‘clear-cut pathology’ (pathology with widely accepted guidance on management) or ‘grey screen’. Results Footballers were aged 13 to 37 years, with 69% aged over 18 and 31% under. The majority of the review population was White European (66%). Of the review population 11% had ‘not normal’ assessments, of these assessments 83% were considered grey screens (by Consultant Cardiologist) requiring further investigation or surveillance. Overall clear-cut pathology was identified in 2%. Conclusions A high proportion of the players (9%) had grey screens. The majority of these were due to ECG or structural abnormalities, which are clinically challenging to differentiate from physiological adaptation of the athletic heart and potentially fatal conditions. The extent to which these findings put the athlete at risk of a life threatening cardiac event is un-?quantified. Team physician’s need to be aware of managing the on-going risk with these patients and ensure suitable ?follow up and assessment on a regular basis to mitigate this.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".