The “Football is Medicine” platform—scientific evidence, large‐scale implementation of evidence‐based concepts and future perspectives
Bibliographic record
Abstract
The idea that football can be used as therapy and as a high-intensity and literally breath-taking training regime goes back centuries. To take one prominent example, the French philosopher Voltaire describes in the Book of Fate (1747), how a patient is cured by playing with a sacred football: “… full-blown and carefully covered with the softest Leather. You must kick this Bladder, Sir, once a Day about your Hall for a whole Hour together, with all the Vigour and Activity you possibly can”, “Ogul, upon making the first Experiment, was ready to expire for want of Breath”, “In short, our Doctor in about 8 days Time, performed an absolute Cure. His Patient was as brisk, active and gay, as One in the Bloom of his Youth.”1 Today, Voltaire and his main character, philosopher Zadig, have been proved right: Football is indeed a breath-taking activity and it can be used as therapy. Albeit today's recommendations suggest a lower training frequency, longer training periods and encourage group-based training, and say that any football can be applied.
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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 | 0.302 | 0.299 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.025 | 0.030 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.020 | 0.024 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".