Bibliographic record
Abstract
OBJECTIVE: The purpose of this article was to examine the preparticipation examination (PPE) with regard to the female athlete. Ever-increasing participation of women in competitive sport has created a requirement for more gender-specific sport medicine knowledge. In particular, physicians and other health care professionals should be aware of the triad of disordered eating, amenorrhea (and other menstrual dysfunction), and osteoporosis (or altered bone mineral density) collectively described as the female athlete triad. Suggested additions to the standard PPE may help identify athletes at risk. DATA SOURCES/METHODS: A literature search was carried out using MEDLINE for years 1966 to 2003, with keywords female athlete triad, PPE, female athlete, eating disorders, amenorrhea, and osteoporosis. Further studies were identified through reference lists. RESULTS: Better recognition and prevention of these problems is essential. At present, there is little evidence-based information available to guide the practicing clinician in this area. It remains to be determined which methods are the most sensitive and specific for detecting the triad disorders, as well as the most economical and time-efficient. CONCLUSIONS: The PPE offers an excellent opportunity to screen for these entities, as well to initiate early treatment. It is recommended that a standardized form (or part of the form) be developed for the female athlete.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".