Selecting a subjective health status measure for optimum utility in everyday orthopaedic practice
Bibliographic record
Abstract
BACKGROUND: The time required to complete patient outcome questionnaires such as the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), the Short Form General Health Survey (SF-36) and the Musculoskeletal Function Assessment (MFA) can sometimes threaten elderly, compromised patients sufficiently to compromise compliance with follow-up clinics. Incomplete questionnaires can also present problems of data bias. A little used (in the USA), patient-friendly questionnaire, the Nottingham Health Profile (NHP) has the potential to reduce the statistical and practical problems associated with the more generally used instruments. We hypothesized that NHP will produce similar results to WOMAC and SF-36 and is more sensitive to small changes in patient outcomes than SF-36 and MFA. METHOD: Twenty-three patients blindly completed WOMAC, SF-36 and NHP questionnaires. Spearman's Rank Order Correlation was used to compare the component scores of each instrument. Simulation of the before and after results of 10 fictitious patient comparing MFA and NHP was conducted using the related sample t-test. RESULTS: Seven of nine correlation coefficients were statistically significant and ranged from 0.711 to 0.901. The significance of the before and after difference on the five-point scale response was P = 0.05 when the NHP was used and P = 0.07 when MFA was used. The before and after difference on 'yes-no' response questions was P < 0.001 when NHP was used but showed no difference when the MFA was used. CONCLUSIONS: Our hypothesis was supported and we suggest that NHP can be used with confidence as an alternative to other patient outcome instruments.
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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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".