The Scoliosis Quandary: Are Radiation Exposures From Repeated X-Rays Harmful?
Bibliographic record
Abstract
X-rays have been the gold standard for diagnosis, evaluation, and management of spinal scoliosis for decades as other assessment methods are indirect, too expensive, or not practical in practice. The average scoliosis patient will receive 10 to 25 spinal X-rays over several years equating to a maximum estimated dose of 10 to 25 mGy. Some patients, those getting diagnosed at a younger age and receiving early and ongoing treatments, may receive up to 40 to 50 X-rays, approaching at most 50 mGy. There are concerns that repeated radiographs given to patients are carcinogenic. Some studies have used the linear no-threshold model to derive cancer-risk estimates; however, it is invalid for low-dose irradiation (ie, X-rays); these estimates are untrue. Other studies have calculated cancer-risk ratios from long-term health data of historic scoliosis cohorts. Since data indicate reduced cancer rates in a cohort receiving a total radiation dose between 50 and 300 mGy, it is unlikely that scoliosis patients would get cancer from repeated X-rays. Moreover, since the threshold for leukemia is about 1100 mGy, scoliosis patients will not likely develop cancers from spinal X-rays. Scoliosis patients likely have long-term health consequences, including cancers, from the actual disease entity itself and not from protracted X-ray radiation exposures that are essential and indeed safe.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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 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".