Common Measures and Analytic Techniques Provide Flawed Assessments of Pain: Modeled Data, and Hip Replacement Study
Bibliographic record
Abstract
OBJECTIVE: To examine commonly used measures and analytic techniques of pain outcomes, using (1) a synthetic model, and (2) a cohort of patients who underwent total hip replacement. METHODS: (1) A synthetic data set was constructed with 110 visual analog scale (VAS) values, 10 for each integer from zero to 10. Random noise was added to simulate measurement variations. Drift through time and a therapeutic trial were simulated. (2) Eighty-six patients were studied before and a mean of 17 months after total hip replacement. Assessments included a VAS pain scale, the Western Ontario and McMaster Universities Osteoarthritis Index, Harris Hip Score, and SF-36 scores. RESULTS: The clinical study mirrored the model. Correlation coefficients among treatment differences measured by the pain subscales of 4 instruments varied from 0.53 to 0.22. Floor effects obscured benefit. "Percentage improvement" created a directional bias, and had a hyperbolic distribution that invalidated means, variances, and related statistics. The best outcomes were undervalued when postoperative pain measures approached zero. Standardized means enabled pooling of data from the different instruments and facilitated measurement of the variations due to treatment, methods, and subjects, and other factors. RESULTS: Outcome measures and analytic techniques are often flawed because of floor and ceiling effects, non-normal distributions, and other problems. Outcomes expressed as ""percentage improvement" are inappropriate; changes should be reported in the observed units. Revisions of standard outcome measures to relate pain with activity can better document outcomes, especially favorable results.
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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 | 0.093 | 0.232 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".