Are Restrictive Medical Radiation Imaging Campaigns Misguided? It Seems So: A Case Example of the American Chiropractic Association’s Adoption of “Choosing Wisely”
Bibliographic record
Abstract
Since the 1980s, increased utilization of medical radiology, primarily computed tomography, has doubled medically sourced radiation exposures. Ensuing fear-mongering media headlines of iatrogenic cancers from these essential medical diagnostic tools has led the public and medical professionals alike to display escalating radiophobia. Problematically, several campaigns including Image Gently, Image Wisely, and facets of Choosing Wisely propagate fears of all medical radiation, which is necessary for the delivery of effective and efficient health care. Since there are no sound data supporting the alleged risks from low-dose radiation and since there is abundant evidence of health benefits from low-doses, these imaging campaigns seem misguided. Further, thresholds for cancer are 100 to 1000-fold greater than X-rays, which are within the realm of natural background radiation where no harm has ever been validated. Here, we focus on radiographic imaging for use in spinal rehabilitation by manual therapists, chiropractors, and physiotherapists as spinal X-rays represent the lowest levels of radiation imaging and are critical in the diagnosis and management of spine-related disorders. Using a case example of a chiropractic association adopting "Choosing Wisely," we argue that these campaigns only fuel the pervasive radiophobia and continue to constrain medical professionals, attempting to deliver quality care to patients.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.009 | 0.012 |
| 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".