Prevalence of Unnecessary Spinal Imaging: Protocol for a Systematic Review and Meta-Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.009 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 it