Prescriptive Clinical Prediction Rules in Back Pain Research: A Systematic Review
Bibliographic record
Abstract
Prescriptive clinical prediction rules (CPRs) are a way of using a small selection of clinical findings to match patients to optimal interventions. A number of CPRs have been developed for use with back pain patients, but these have not been systematically reviewed. The purpose of this review was to evaluate existing CPRs against established criteria to determine the quality of the studies and the overall development of the CPR against a set number of stages. Medline was searched up until June 2008, and 16 studies were reviewed that related to 9 different CPRs. These studies investigated and attempted to find clinical characteristics for responders to manipulation, stabilization exercise, physical therapy, chiropractic, traction, rehabilitation, usual care, and zygapophyseal joint injections. Eleven of these studies related to the derivation stage and five to the validation stage. The manipulation and stabilization CPRs had been the most studied. The derivation studies were mostly high quality, whereas none of the validation studies were. Some of the validation studies did not provide evidence that validated the CPR. Most of these CPRs need further evaluation before they can be applied clinically; most did not pass the lowest level of evidence hierarchy. As regards the manipulation CPR, evidence to date for its clinical utility is limited and contradictory. For the stabilization CPR, there was limited evidence that it may be considered but only with caution and in similar patients. Overall, there is limited evidence to support the general application of spinal CPRs.
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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 | 0.054 | 0.235 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".