Bibliographic record
Abstract
Radiotherapy, with close to a million courses delivered per year in North America, is a very safe and effective intervention for a devastating disease. However, although rare, several deeply regrettable incidents have occurred in radiotherapy and have rightly been the subject of considerable public interest. Partly in response to reports of these incidents a variety of authoritative organizations across the globe has harnessed the expertise amongst their members in attempts to identify the measures that will make radiotherapy safer. While the intentions of all these organizations are clearly good it is challenging for the health care providers in the clinic to know where to start with so much advice coming from so many directions. Through a mapping exercise we have identified commonalities between recommendations made in seven authoritative documents and identified those issues most frequently cited. The documents reviewed contain a total of 117 recommendations. Using the 37 recommendations in "Towards Safer Radiotherapy" as the initial base layer, recommendations in the other documents were mapped, adding to the base layer to accommodate all the recommendations from the additional six documents as necessary. This mapping exercise resulted in the distillation of the original 117 recommendations down to 61 unique recommendations. Twelve topics were identified in three or more of the documents as being pertinent to the improvement of patient safety in radiotherapy. They are, in order of most to least cited: training, staffing, documentation, incident learning, communication, check lists, quality control and preventive maintenance, dosimetric audit, accreditation, minimizing interruptions, prospective risk assessment, and safety culture. This analysis provides guidance for the selection of those activities most likely to enhance safety and quality in radiotherapy based on the frequency of citation in selected recent authoritative literature.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".