Prescriptive Clinical Prediction Rules in Back Pain Research: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it