Laboratory medicine and sports: between Scylla and Charybdis
Bibliographic record
Abstract
Laboratory medicine is complex and contributes to the diagnosis, therapeutic monitoring and follow-up of acquired and inherited human disorders. The regular practice of physical exercise provides important benefits in heath and disease and sports medicine is thereby receiving growing focus from almost each and every clinical discipline, including laboratory medicine. Sport-laboratory medicine is a relatively innovative branch of laboratory science, which can provide valuable contributions to the diagnosis and follow-up of athletic injuries, and which is acquiring a growing clinical significance to support biomechanics and identify novel genomics and "exercisenomics" patterns that can help identify specific athlete's tendency towards certain types of sport traumas and injuries. Laboratory medicine can also provide sport physicians and coaches with valuable clues about personal inclination towards a certain sport, health status, fitness and nutritional deficiencies of professional, elite and recreational athletes in order to enable a better and earlier prediction of sport injuries, overreaching and overtraining. Finally, the wide armamentarium of laboratory tests represents the milestone for identifying cheating athletes in the strenuous fight against doping in sports.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".