Internet-based ICRP resource for healthcare providers on the risks and benefits of medical imaging that uses ionising radiation
Bibliographic record
Abstract
The purpose of the International Commission on Radiological Protection (ICRP) Committee 3 Working Party was to update the 2001 web-based module 'Radiation and your patient: a guide for medical practitioners' from ICRP. The key elements of this task were: to clearly identify the target audience (such as healthcare providers with an emphasis on primary care); to review other reputable sources of information; and to succinctly publish the contribution made by ICRP to the various topics. A 'question-and-answer' format addressing practical topics was adopted. These topics included benefits and risks of imaging using ionising radiation in common medical situations, as well as pertaining to specific populations such as pregnant, breast-feeding, and paediatric patients. In general, the benefits of medical imaging and related procedures far outweigh the potential risks associated with ionising radiation exposure. However, it is still important to ensure that the examinations are clinically justified, that the procedure is optimised to deliver the lowest dose commensurate with the medical purpose, and that consideration is given to diagnostic reference levels for particular classes of examinations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.297 | 0.174 |
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".