Do not routinely offer imaging for uncomplicated low back pain
Bibliographic record
Abstract
### What you need to know The past two decades have seen a paradigm shift in the way we use imaging when managing low back pain (LBP). Imaging was once a routine part of the diagnostic workup for most cases of LBP. Evidence now indicates that imaging is useful only in the small subgroup of patients for whom there is suspicion of red flag conditions. These conditions include cancer, infection, inflammatory disease, fracture, and severe neurological deficits—which together account for only 5-10% of LBP presentations in primary care.1 For the remaining 90-95% of LBP cases (called non-specific or uncomplicated LBP), imaging will not guide management and can cause more harm than benefit. International guidelines2 and “Choosing Wisely” campaigns now encourage a diagnostic triage approach to identify those patients who require imaging (box 1). Given these advances in knowledge, imaging rates for LBP should be decreasing, but recent systematic reviews show the opposite, reporting that imaging has increased over the past 20 years3 and that at least a third of all images are unnessary.4 Box 1 ### “Choosing Wisely” statements from different countries related to avoiding the use of routine imaging for low back painRETURN TO TEXT
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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 | 0.004 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.059 | 0.023 |
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".