A prioritization framework for the analysis of near misses in radiation oncology
Bibliographic record
Abstract
INTRODUCTION: The term near miss implies the aversion of a harm event but often there is a lack of evidence when establishing a link between a failure in process and potential harm. The focus of this study was to use reported incident data to inform a prioritization framework for the triage of near miss events in a radiation therapy program. MATERIALS AND METHODS: Actual and near miss events during the study period were categorized using thematic analysis based on incident types. Near miss were characterized based upon their potential to result in harm to the patient using the concepts of failure modes and Analytic Hierarchy Process (AHP) theory. Near miss events were assessed for occurrence, detection and the potential impact and then assigned a summative normalized score reflecting prioritization recommendations, the normalized 10 point score (NTPS). RESULTS: = 20) were attributed to near misses. 107 total events we analyzed using the framework with a maximum NTPS of 4 achieved across all event types. Of the 47 actual events 100% received a NTPS of 3 or greater. Of the 60 near miss invents 47% received an NTPS less than or equal to 1. Finally 15% of near miss events received a NTPS of 3 or greater. CONCLUSIONS: Near miss events provide a unique opportunity for learning however, can yield a great deal of data potentially limiting the resources for effective incident learning. A FMEA and AHP based prioritization framework for the triage of near miss events, including the likelihood of occurrence, probability of the event to go undetected and the potential impact if the incident did occur, allows for the optimal focus of programmatic resources in the analysis of these events.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".