Sport Medicine Diagnostic Coding System (SMDCS) and the Orchard Sports Injury and Illness Classification System (OSIICS): revised 2020 consensus versions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Coding in sports medicine generally uses sports-specific coding systems rather than the International Classification of Diseases (ICD), because of superior applicability to the profile of injury and illness presentations in sport. New categories for coding were agreed on in the 'International Olympic Committee (IOC) consensus statement: Methods for recording and reporting of epidemiological data on injury and illness in sports 2020.' We explain the process for determining the new categories and update both the Sport Medicine Diagnostic Coding System (SMDCS) and the Orchard Sports Injury and Illness Classification System (OSIICS) with new versions that operationalise the new consensus categories. The author group included members from an expert group attending the IOC consensus conference. The primary authors of the SMDCS (WM) and OSIICS (JO) produced new versions that were then agreed on by the remaining authors using expert consensus methodology. The SMDCS and OSIICS systems have been adjusted and confirmed through a consensus process to align with the IOC consensus statement to facilitate translation between the two systems. Problematic areas for defining body part categories included the groin and ankle regions. For illness codes, in contrast to the ICD, we elected to have a taxonomy of 'organ system/region' (eg, cardiovascular and respiratory), followed by an 'aetiology/pathology' (eg, environmental, infectious disease and allergy). Companion data files have been produced that provide translations between the coding systems. The similar structure of coding underpinning the OSIICS and SMDCS systems aligns the new versions of these systems with the IOC consensus statement and also facilitates easier translation between the two systems. These coding systems are freely available to the sport and exercise research community.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it