Diagnosis Value of Patient Evaluation Components Applicable in Primary Care Settings for the Diagnosis of Low Back Pain: A Scoping Review of Systematic Reviews
Bibliographic record
Abstract
Low back pain ranks as the leading cause of years lived with disability worldwide. Although best practice guidelines share a consistent diagnostic approach for the evaluation of patients with low back pain, confusion remains as to what extent patient history and physical examination findings can inform management strategies. The aim of this study was to summarize evidence investigating the diagnostic value of patient evaluation components applicable in primary care settings for the diagnosis of low back pain. To this end, peer-reviewed systematic reviews were searched in MEDLINE, CINAHL, PsycINFO and Cochrane databases from 1 January 2000 to 10 April 2023. Paired reviewers independently reviewed all citations and articles using a two-phase screening process and independently extracted the data. Of the 2077 articles identified, 27 met the inclusion criteria, focusing on the diagnosis of lumbar spinal stenosis, radicular syndrome, non- specific low back pain and specific low back pain. Most patient evaluation components lack diagnostic accuracy for the diagnosis of low back pain when considered in isolation. Further research is needed to develop evidence-based and standardized evaluation procedures, especially for primary care settings where evidence is still scarce.
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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 | 0.030 | 0.167 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".