Adherence to spinal imaging guidelines and utilization of lumbar spine diagnostic imaging for low back pain at a Canadian Chiropractic College: a historical clinical cohort study
Bibliographic record
Abstract
BACKGROUND: Diagnostic imaging is useful for assessing low back pain (LBP) when a clinician suspects a specific underlying pathology. Evidence-based imaging guidelines assist clinicians in appropriately determining the need for imaging when assessing LBP. A previous study reported high adherence to three clinical guidelines, with utilization rate of 12.3% in imaging of LBP patients attending a chiropractic teaching clinic. A new imaging guideline for spinal disorders has been published and used in teaching. Thus, the aims of our study were to assess the adherence to the new guideline and X-ray utilization in new episodes of LBP. METHODS: We conducted a historical clinical cohort study using patient electronic health record audits at seven teaching clinics over a period of 20 months. Records of patients who were at least 18 years of age, presented with a new onset of LBP, and consented to data collection were included. Abstracted data included patient demographics, the number and type of red flags, and the decision to image. Rate of guideline adherence (proportion of those not recommended for imaging, given no red flags) and rate of image utilization were descriptively analyzed. RESULTS: We included 498 patients in this study. At least 81% of included patients had one or more red flags reported. The most commonly reported individual red flag was age ≥ 50 (43.8%) followed by pain at rest (15.7%). In those referred for imaging, age ≥ 50 (93.3%) was the most frequently reported red flag. No red flag(s) were identified in 93 patient records, and none were referred for imaging of their LBP, yielding an adherence rate of 100% (95% CI 96, 100%). A total of 17 of 498 patients were recommended for imaging for their low back pain, resulting in an imaging utilization rate of 3.4% (95% CI 1.8, 5.0%). CONCLUSION: The imaging utilization rate was 3.4%, lower than 12.3% previously reported at a chiropractic teaching clinic. None without red flags were referred for imaging, yielding a 100%, adherence rate to current LBP imaging guidelines. Future research should consider currency of guideline, accuracy of red flags and factors influencing clinicians' decision, when assessing imaging adherence rates.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".