Poster — Thur Eve — 10: User Dependence of Three Radiation Oncology Incident Reporting Ranking Systems
Bibliographic record
Abstract
The recent public scrutiny of errors in radiation therapy has underscored the need for incident reporting and classification mechanisms in the field. Several such systems have been proposed by various national and international organizations. The utility of any incident classification scheme ultimately will depend on how uniformly the system can be used and interpreted. Here we present a systematic assessment of these incident classification systems. Three different incident classification schemes were used in this evaluation. The Autorite de Surete Nucleaire together with the Socitete Fracaise de Radiotherapie Oncologique (ASN‐SFRO)); the British Institute of Radiology (BIR); the American Association of Physicists in Medicine Task Group 100 (TG100). We have applied these severity rankings to incidents taken from a compendium of errors published by the World Health Organization (WHO). This compendium provided the details for twenty radiotherapy incidents and twenty‐eight “near‐misses”. Six individuals were asked to rank each of these incidents using each of the three classification schemes. Study participants included four physicists and two radiation oncologist. The Friedman test was applied to test the null hypothesis that the rankings are consistently applied by all observers. The results suggest that the six rankers did not apply the ranking method consistently. This suggests that more quantitative methods are needed to score radiotherapy incidents such that better consistency can be achieved in incident reporting.
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 | 0.091 | 0.249 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".