What's all that noise?The effect of co-morbidity on health outcome questionnaire results after knee arthroplasty
Bibliographic record
Abstract
BACKGROUND: We modified the Charnley Classification for hips to facilitate its use with knee arthroplasty patients and investigated what affect the different classes of co-morbidity had on the results of a spectrum of outcome questionnaires. PATIENTS AND METHODS: 3600 patients from the Swedish Knee Arthroplasty Registry were surveyed by post with a variety of questionnaires ranging from multiple-item general health, to a single-item knee arthroplasty specific questionnaire. All patients also completed a co-morbidity questionnaire, from which a modified Charnley Classification was generated for each patient. We then investigated the correlation and relationship between the results of the questionnaires and the different classes of co-morbidity. RESULTS: The results of the questionnaires tested varied significantly by Charnley Class, regardless of the specificity of the questionnaire used. INTERPRETATION: We suggest that co-morbidity should be taken into account in outcome studies utilizing general health or disease/site specific questionnaires.
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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 | 0.028 | 0.188 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".