Statistical Significance Versus Clinical Importance
Bibliographic record
Abstract
In Brief Study Design. Critical appraisal of the literature. Objecives. The objective of this study was to assess if results of back pain trials are statistically significant and clinically important. Summary of Background Data. There seems to be a discrepancy between conclusions reported by authors and actual results of randomized controlled trials. Little attention has been paid to the problem of over-reporting of conclusions. Methods. All 43 trials of the Cochrane review on exercise therapy for low back pain were included. Descriptive analyses were conducted. Results. Eighteen trials reported positive conclusions in favor of exercise. Only six of the 43 studies showed both clinically important and statistically significant differences in favor of the exercise groups on function, and 4 on pain. Conclusion. It seems that many conclusions of studies of exercise therapy for chronic low back pain have been based on statistical significance of results rather than on clinical importance and, consequently, may have been too positive. Authors of trials should report not only statistical significance of results but also clinical importance. The Cochrane review on exercise therapy for low back pain showed that many conclusions of the 61 included randomized trials have been based on statistical significance of results rather than on clinical importance and, consequently, may have been too positive.
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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 | 0.475 | 0.744 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.019 | 0.011 |
| Bibliometrics | 0.027 | 0.011 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".