Appropriateness of imaging decisions for low back pain presenting to the emergency department: a retrospective chart review study
Bibliographic record
Abstract
BACKGROUND: Imaging for low back pain is widely regarded as a target for efforts to reduce low-value care. OBJECTIVE: We aimed to estimate the prevalence of the overuse and underuse of lumbar imaging in patients presenting with low back pain to the emergency department (ED). METHODS: This was a retrospective chart review study of five public hospital EDs in Sydney, Australia, in 2019-20. We reviewed the clinical charts of consecutive adult patients who presented with a complaint of low back pain and extracted clinical features relevant to a decision to request lumbar imaging. We estimated the proportion of encounters where a decision to request lumbar imaging was inappropriate (overuse) or where a clinician did not request an appropriate and informative lumbar imaging test when indicated (underuse). RESULTS: Six hundred and forty-nine patients presented with a complaint of low back pain, of which 158 (24.3%) were referred for imaging. Seventy-nine (12.2%) had a combination of features suggesting that lumbar imaging was indicated according to clinical guidelines. The prevalence of overuse and underuse of lumbar imaging was 8.8% (57 of 649 cases, 95% CI 6.8-11.2%) and 4.3% (28 of 649 cases, 95% CI 3.0-6.1%), respectively. Thirteen cases were classified as underuse because the patients were referred for uninformative imaging modalities (e.g. referred for radiography for suspected cauda equina syndrome). CONCLUSION: In this study of emergency care, there was evidence of not only overuse of lumbar imaging but also underuse through failure to request lumbar imaging when indicated or referral for an uninformative imaging modality. These three issues seem more important targets for quality improvement than solely focusing on overuse.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".