Swiss Orthopaedics Minimal Dataset: First Pilot Report of Reliability and Validity
Bibliographic record
Abstract
Background. The Swiss Orthopaedics Minimal Dataset (SOMD) was launched seven years ago. It is a standardized, generic, and patient-reported outcome questionnaire, comprising ten items (location of disease, pain within the past four weeks, limitations at work/leisure/sleep/autonomy, subjective value of a body part, employment status, work disability (sick leave/pension), and household support). We conducted this study about the SOMD to report its reliability, validity, and clinical applicability. Methods. A retrospective observational cohort study was conducted. The test-retest study population (n = 60; lost to follow-up: n = 7 (12%)) was drawn from three retirement homes (in 2013), while the test study population (n = 14,180; excluded (e.g., duplicates): n = 1,990 (14%)) consisted of patients from a university hospital (in 2014–2017). In the test-retest study population, the same questionnaire was completed twice (at days 0 and 7). In the test study population, only the first questionnaire was included (to avoid duplicates). In a subgroup of the test study population (n = 302), only those patients who completed the SOMD and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) of the hip within 14 days were considered (to minimize recall bias). Reliability (test-retest and internal consistency), criterion validity for the item of pain, and return rates were analyzed. Results. The test-retest study population (n = 53) showed very high test-retest reliability for all tested items of the SOMD (intraclass correlation coefficient = 0.96–1.00 (95% confidence interval 0.93–1.00), p < 0.001 ). The test study population (n = 12,190) revealed good internal consistency reliability for all ten items (Cronbach’s alpha = 0.80). The return rates of the SOMD were improvable (43% in 2016 and 31% in 2017). The subgroup of the test study population (n = 302) displayed a borderline acceptable criterion validity (correlation of the item of pain between SOMD and WOMAC hip: rho = 0.62, p < 0.001 ). Conclusion. This is the first report about the validation of the SOMD. A relatively high reliability (test-retest and internal consistency), borderline acceptable (criterion) validity for the item of pain, and improvable clinical implementation were observed. This analysis serves as the basis for a structured modification of the SOMD to improve its value.
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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 | 0.015 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".