A Critical Review of Reviews on the Treatment of Chronic Low Back Pain
Bibliographic record
Abstract
STUDY DESIGN: Systematic literature review. OBJECTIVE: To critically appraise the methodology of systematic reviews of conservative therapies for chronic nonspecific low back pain and to study the relation between the methodologic quality and other characteristics of these reviews. SUMMARY OF BACKGROUND DATA: Systematic reviews offer a concise summary of the evidence on treatment effectiveness, but flaws in their methodology can lead to invalid conclusions with serious implications for quality of patient care. METHODS: Searches of MEDLINE, EMBASE, Psychinfo, and the Cochrane Library were conducted. Titles, abstracts, and articles were reviewed by two blinded authors using three inclusion criteria: 1) chronic nonspecific low back pain, 2) systematic review, and 3) conservative treatment intervention. Data were extracted from each review by three authors. RESULTS: The search strategy retrieved 1102 titles and abstracts; 109 met inclusion criteria. A review of the full text of these articles excluded an additional 73 articles. Data abstraction and methodologic assessment were conducted on 36 articles reviewing 19 discrete interventions. The average quality score was 4.1, ranging from 1 (low) to 7 (high). There was a trend for recent reviews to be of higher quality. Fifty-six percent of the reviews had positive conclusions, but they had lower quality scores compared with those that had negative or uncertain conclusions. There were 27 (73%) qualitative and 10 (27%) quantitative summaries of results. CONCLUSIONS: Although the overall quality of systematic reviews was satisfactory, the quality of the individual papers included in the reviews varied considerably. The reviews often provided contradictory evidence on the effectiveness of a wide range of commonly used conservative interventions for chronic nonspecific low back pain. These findings illustrate the pitfalls of systematic reviews where there are a number of low-quality trials and underscore the need for high-quality primary trials that will allow for more conclusive reviews.
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.
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 | 0.083 | 0.284 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.011 |
| Bibliometrics | 0.029 | 0.024 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".