AAPM task group report 288: Recommendations for guiding radiotherapy event narratives
Bibliographic record
Abstract
Incident reporting and learning systems provide an opportunity to identify systemic vulnerabilities that contribute to incidents and potentially degrade quality. The narrative of an incident is intended to provide a clear, easy to understand description of an incident. Unclear, incomplete or poorly organized narratives compromise the ability to learn from them. This report provides guidance for drafting effective narratives, with particular attention to the use of narratives in incident reporting and learning systems (IRLS). Examples are given that compare effective and less than effective narratives. This report is mostly directed to organizations that maintain IRLS, but also may be helpful for individuals who desire to write a useful narrative for entry into such a system. Recommendations include the following: (1) Systems should allow a one- or two-sentence, free-text synopsis of an incident without guessing at causes; (2) Information included should form a sequence of events with chronology; and (3) Reporting and learning systems should consider using the headings suggested to guide the reporter through the narrative: (a) incident occurrences and actions by role; (b) prior circumstances and actions; (c) method by which the incident was identified; (d) equipment related details if relevant; (e) recovery actions by role; (f) relevant time span between responses; (g) and how individuals affected during or immediately after incident. When possible and appropriate, supplementary information including relevant data elements should be included using numerical scales or drop-down choices outside of the narrative. Information that should not be included in the narrative includes: (a) patient health information (PHI); (b) conjecture or blame; (c) jargon abbreviations or details without specifying their significance; (d) causal analysis.
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 | 0.116 | 0.250 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.014 | 0.008 |
| Research integrity | 0.017 | 0.010 |
| Insufficient payload (model declined to judge) | 0.065 | 0.127 |
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".