Are general practitioners referring patients with low back pain for CTs appropriately according to the guidelines: a retrospective review of 3609 medical records in Newfoundland using routinely collected data
Bibliographic record
Abstract
BACKGROUND: CT Imaging is often requested for patients with low back pain (LBP) by their general practitioners. It is currently unknown what reasons are common for these referrals and if CT images are ordered according to guidelines in one province in Canada, which has high rates of CT imaging. The objective of this study is to categorise lumbar spine CT referrals into serious spinal pathology, radicular syndrome, and non-specific LBP and evaluate the appropriateness of CT imaging referrals from general practitioners for patients with LBP. METHODS: A retrospective medical record review of electronic health records was performed in one health region in Newfoundland and Labrador, Canada. Inclusion criteria were lumbar spine CT referrals ordered by general practitioners for adults ≥18 years, and performed between January 1st-December 31st, 2016. Each CT referral was identified from linked databases (Meditech and PACS). To the study authors' knowledge, guidelines regarding when to refer patients with low back pain for CT imaging had not been actively disseminated to general practitioners or implemented at clinics/hospitals during this time period. Data were manually extracted and categorised into three groups: red flag conditions (judged to be an appropriate referral), radicular syndrome (judged be unclear appropriateness), or nonspecific LBP (determined to be inappropriate). RESULTS: Three thousand six hundred nine lumbar spine CTs were included from 2016. The mean age of participants was 54.7 (SD 14 years), with females comprising 54.6% of referrals. 1.9% of lumbar CT referrals were missing/unclear, 6.5% of CTs were ordered on a red-flag suspicion, 75.6% for radicular syndromes, and 16.0% for non-specific LBP; only 6.5% of referrals were clearly appropriate. Key information including patient history and clinical exams performed at appointment were often missing from referrals. CONCLUSION: This audit found high proportions of inappropriate or questionable referrals for lumbar spine CT and many were missing information needed to categorise. Further research to understand the drivers of inappropriate imaging and cost to the healthcare system would be beneficial.
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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 | 0.008 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".