Radiophobia: 7 Reasons Why Radiography Used in Spine and Posture Rehabilitation Should Not Be Feared or Avoided
Bibliographic record
Abstract
Evidence-based contemporary spinal rehabilitation often requires radiography. Use of radiography (X-rays or computed tomography scans) should not be feared, avoided, or have their exposures lessened to decrease patient dose possibly jeopardizing image quality. This is because all fears of radiation exposures from medical diagnostic imaging are based on complete fabrication of health risks based on an outdated, invalid linear model that has simply been propagated for decades. We present 7 main arguments for continued use of radiography for routine use in spinal rehabilitation: (1) the linear no-threshold model for radiation risk estimates is invalid for low-dose exposures; (2) low-dose radiation enhances health via the body's adaptive response mechanisms (ie, radiation hormesis); (3) an X-ray with low-dose radiation only induces 1 one-millionth the amount of cellular damage as compared to breathing air for a day; (4) radiography is below inescapable natural annual background radiation levels; (5) radiophobia stems from unwarranted fears and false beliefs; (6) radiography use leads to better patient outcomes; (7) the risk to benefit ratio is always beneficial for routine radiography. Radiography is a safe imaging method for routine use in patient assessment, screening, diagnosis, and biomechanical analysis and for monitoring treatment progress in daily clinical practice.
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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 | 0.013 | 0.035 |
| 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 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".