Diagnostic and management concordance between chiropractors and neurosurgeons for patients with low back pain
Bibliographic record
Abstract
Low back pain is the leading contributor to disability worldwide and a major cause of primary care visits. Alternative models of care delivery drawing on musculoskeletal experts' skills and knowledge have received increasing attention for their potential ability to improve timely access to appropriate healthcare for patients with musculoskeletal disorders. The aim of this study was to evaluate diagnostic and management concordance between chiropractors, known as musculoskeletal experts, and neurosurgeons for patients with low back pain. Before being seen by a neurosurgeon, 101 eligible participants (mean age: 60.32 years) were evaluated by a chiropractor. Overall diagnostic agreement between chiropractors and neurosurgeons was 74.7%, with a moderate inter-rater diagnosis agreement (κ = 0.51; 95%CI [0.35-0.68]). Chiropractors were significantly less likely to attribute a diagnosis of non-specific LBP to participants (31.6%) compared to neurosurgeons (43.2%) (p = 0.02), with an agreement proportion of 80.0%. Overall management agreement was 82.0%, indicating that chiropractors possess good skills in triaging patients with low back pain, which can optimize patient trajectories by accelerating management of non-surgical cases and reducing waiting lists for spine surgery consultations. Prospective studies are needed to evaluate the impact of a chiropractor-informed triage on clinical outcomes and healthcare utilization for patients with low back pain.
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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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".