The Role of Classification of Chronic Low Back Pain
Bibliographic record
Abstract
STUDY DESIGN: Systematic review. OBJECTIVE: To describe the various ways chronic low back pain (CLBP) is classified, to determine if the classification systems are reliable and to assess whether classification-specific interventions have been shown to be effective in treating CLBP. SUMMARY OF BACKGROUND DATA: A classification system by which individual patients with CLBP could be identified and directed to an effective treatment protocol would be beneficial. Those systems that direct treatment have the greatest potential influence on patient outcomes. METHODS: A systematic search was conducted in MEDLINE and the Cochrane Collaboration Library for English language literature published through January 2011. We included articles that specifically described a clinical classification system for CLBP, reported on the reliability of a classification system, or evaluated the effectiveness of classification-specific interventions. RESULTS: A total of 60 articles were initially reviewed. We identified 28 classification systems that met inclusion criteria: 16 diagnostic systems, 7 prognostic systems, and 5 treatment-based systems. In addition, we found 10 randomized controlled trials of CLBP treatment from which we compared inclusion and exclusion criteria. Treatment-based systems were all directed at nonoperative management. Four of the 5 treatment-based systems underwent reliability testing and were found to have interobserver agreement of 70% to 100%. Reliability increased with training and familiarity with a given classification. As the number of subgroups within a classification increased, interobserver agreement decreased. Function and pain were similar between patients treated with the McKenzie classification system and those treated with dynamic strengthening training after 8 months of follow-up in one randomized controlled trial. One prospective cohort study reported better pain and function using the Canadian Back Institute Classification system than with standard rehabilitation. An analysis of the admission criteria to recent randomized studies with either nonoperative care or another surgical intervention provided a methodology for refining criteria to be met by patients considering surgery. CONCLUSION: There currently are many classification systems for CLBP; some that are descriptive, some prognostic, and some that attempt to direct treatment. We recommend that no one classification system be adopted for all purposes. We further recommend that future efforts in developing a classification system focus on one that helps to direct both surgical and nonsurgical treatments. CLINICAL RECOMMENDATIONS: There currently are many classification systems for CLBP; some that are descriptive, some prognostic, and some that attempt to direct treatment. We recommend that no one classification system be adopted for all purposes. We further recommend that future efforts in developing a classification system focus on one that helps to direct both surgical and nonsurgical treatments.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".