A successful, cost-effective low back pain triage system: a pilot study
Bibliographic record
Abstract
Background: Effective triage - directing patients with low back pain to appropriate treatment or correct referral - is fundamental to quality care. Without guidelines, a physician's initial decision may lead to unnecessary investigation, unneeded intervention or unwarranted consultation.Methods: To compare the functional outcomes of patients triaged by a classification based on clinical presentation with those of patients selected at the clinicians’ discretion, an insurance-owned hospital network employed forty-seven specially-trained physical therapists, working within participating primary care practices, to classify low back pain patients into specific Patterns of Pain. Between October 2017 and April 2019, the primary care physicians used this classification, derived entirely from the patient's history and physical examination, to direct subsequent treatment for 260 consecutive low back pain patients. Patients with systemic symptoms, recent substantial trauma or non-mechanical diagnoses indicative of spinal infections or possible malignancy were excluded. Functional outcome measures were spinal imaging, opioid use, length of treatment and number of visits, back-related unplanned care, frequency of spinal surgery and back-related episode cost. These were compared with a control group of 256 propensity-matched patients and, for assessing the financial impact, with a historic cohort of 111 previously treated, non-classified patients.Results: Spinal imaging: study group 24.5%; controls 42.2% (P< .001). Narcotic use: study group 4.6%; controls 13.3% (P< .001). Treatment length: study group 62.2 days; controls 74.5 days (P=.10). Treatment visits: study group 1528 visits; controls 2,046 visits (P=.003). Unplanned care: study group 1.9%; controls 12.8% (P< .001). Spine surgery: study group 15.4%; controls 26.2% (P=.005). Episode cost: study group $1453; controls $2334 (P=.005).Conclusions: A well-defined clinically-based triage system produced meaningful reductions in imaging, opioid use, treatment duration, unplanned interventions, surgery and cost of care.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".