Learning from laser safety incidents in the medical setting
Bibliographic record
Abstract
After the immediate response to laser safety incident; assessing and treating injury, as well as removing immediate hazards posed to other patients and care providers; the focus shifts to a thorough investigation of the event to determine ways to mitigate future risk.This presentation will summarize current approaches in the ongoing management of adverse events in laser safety; investigation, analysis and formulation of strong recommendations to improve laser safety in the medical setting.The new view of safety challenges laser safety officers to look at systems factors rather than simply concluding that the event was the result of ‘operator error’. A method of analyzing adverse events, known as a ‘constellation mapping’ will be introduced and applied to an adverse event in laser surgery that has been reported to the British Medical Laser Association. This is a powerful tool in shifting from blaming individuals (“operator error”), to identifying system fixes that will prevent future adverse events.Design of recommendations will also be reviewed, using recognized hierarchy of effectiveness to arrive at the most impactful recommendations.Lastly, the importance of disseminating lessons learned will be emphasized.
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 | 0.041 | 0.155 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.013 | 0.025 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".