Prevalence of Unnecessary Spinal Imaging: Protocol for a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
In recent years, the escalating demand for imaging services has led to a notable increase in low-value imaging, with estimates suggesting that 20 to 50% of all imaging procedures worldwide may be unnecessary. This trend is supported by data from the 2019/2020 Canadian Medical Imaging Inventory, which reported significant increases in the utilization of MRI, PET-CT, and SPECT-CT units per million population since 2010/2011. Specifically, instances of unnecessary spinal imaging in the evaluation of LBP continue to surge despite guidelines advising against routine imaging without red-flag symptoms. This widespread practice not only incurs substantial costs, but raises concerns about the optimal use of such medical interventions. This systematic review aims to address this gap by quantifying the prevalence of unnecessary spinal imaging.
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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 | 0.046 | 0.099 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.021 | 0.032 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.062 | 0.006 |
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".