Radiation Dose Issues in Longitudinal Studies Involving Computed Tomography
Bibliographic record
Abstract
Computed tomography (CT) examinations are increasingly used for clinical diagnostic and research purposes, as they provide in vivo anatomic information similar to that provided by gross anatomy. In conjunction with physiologic maneuvers or contrast media, CT may also provide in vivo physiologic information. Using calibrated acquisition protocols, accurate noninvasive measurements of tissue density, air volume, blood volume, and capillary perfusion can be performed. Serial CT scans can provide longitudinal measurements indicative of disease progression or regression, allowing noninvasive assessment of treatment effects. However, the X-ray radiation associated with CT has been associated with a small but significant increased risk of malignancy, which may be fatal. Large studies have detected this small risk, which appears to be related to the cumulative radiation dose of all previous exposures in a linear fashion. It has been shown that the risk from a given radiation exposure is greater in young people and females compared with older males. The combination of these two risk-enhancing factors, found in pregnant females, provides the greatest risk. Radiation risk decreases with increasing age for both men and women, asymptotically approaching zero. Radiation risk can be calculated using dose metrics provided on current CT scanners as outlined in this article. Ethically, given that radiation is associated with measurable risk, clinically indicated and research CT examinations must provide an increase in knowledge that has substantial benefit to the subject. This benefit should be related to the potential of saving of life or to the prevention or mitigation of serious disease.
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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 | 0.032 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".