Advancing the Preparticipation Physical Evaluation
Bibliographic record
Abstract
: While the preparticipation physical evaluation (PPE) is widely accepted, its usage and content are not standardized. Implementation is affected by cost, access, level of participation, participant age/sex, and local/regional/national mandate. Preparticipation physical evaluation screening costs are generally borne by the athlete, family, or club. Screening involves generally agreed-upon questions based on expert opinion and tested over decades of use. No large-scale prospective controlled tracking programs have examined PPE outcomes. While the panel did not reach consensus on electrocardiogram (ECG) screening as a routine part of PPE, all agreed that a history and physical exam focusing on cardiac risk is essential, and an ECG should be used where risk is increased. The many areas of consensus should help the American College of Sports Medicine and Fédération Internationale du Médicine du Sport in developing a universally accepted PPE. An electronic PPE, using human-centered design, would be comprehensive, would provide a database given that PPE is mandatory in many locations, would simplify PPE administration, would allow remote access to clinical data, and would provide the much-needed data for prospective studies in this area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".