It is time to stop causing harm with inappropriate imaging for low back pain
Bibliographic record
Abstract
Inappropriate imaging for low back pain (LBP) can cause harm in three ways: 1. Misinterpretation of results by clinicians resulting in unhelpful advice, needless subsequent investigations (downstream testing) and invasive interventions, including surgery;1 2. Misinterpretation of results by patients resulting in catastrophisation, fear and avoidance of movement and activity, and low expectations of recovery;2 3. Side effects such as exposure to radiation.3 Problems associated with excessive imaging for LBP are well recognised (http://www.choosingwisely.org) and useful evidence-based guidelines have been developed to help clinicians determine when investigation is appropriate.3 However, currently, 42% of patients with LBP receive an X-ray, CT or MRI within 1 year of diagnosis, and of these, 80% receive imaging within 1 month of presentation.4 The uptake of imaging guidelines is likely to be similarly insufficient among the sports medicine community, where lumbar imaging is frequently used. As well as recognising when imaging is appropriate, evidence-based reporting and interpretation of imaging findings is critical. The contents of …
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.015 | 0.026 |
| Insufficient payload (model declined to judge) | 0.012 | 0.014 |
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".