Orchard Sports Injury and Illness Classification System (OSIICS) version 16: Updated female athlete codes and Italian and Spanish translations
Bibliographic record
Abstract
Background: The Orchard Sports Injury and Illness Classification System (OSIICS) requires regular update to; stay relevant to changes required for optimal code recording; to add additional language translations as they become available and add female-specific codes to reflect contemporary clinical diagnosis and the increase in women's sport participation. Methods: Codes were added to the existing version 15 after the assembly of an ad hoc panel of co-authors. Being a revision that only added codes without changing any structure, informal methodology, primarily conducted by email discussion amongst the author group, was used to resolve issues. Two authors who were native Italian and Spanish speakers, and fluent in English were used to create the respective language translations. Results: The area of greatest deficiency for OSIICS versions 13-15 was in coding for breast conditions. To fit in with the existing consensus categories, the injury format used was CxBxx with a third character B to signify breast within the chest region. For breast illness/medical disorders, the coding format was MxxBx. Conclusion: Although consideration was given to creating a new, standalone category for breast injuries, due to the formalised alignment between OSIICS and Sports Medicine Diagnostic Coding System it was decided such a change requires the IOC consensus panel to re-convene. Additionally, the medical/illness categories need consideration of whether breast disorders best fit in the endocrine or genitourinary system, or if an expanded medical category is needed for female reproductive medical conditions.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".