468 Surgical Fires and Burns: A 5-year Analysis of Medico-legal Cases
Bibliographic record
Abstract
Surgical fires and unintended intra-operative burns are “never events”, yet they continue to occur. Medico-legal data provide unique information on the nature of surgical fires and burns and their contributing factors, which can inform strategies for burn prevention. We extracted five years (2012-2016) of data on closed medico-legal cases involving surgical fires and burns from a database which provides medico-legal support to over 95% of Canadian physicians. We performed a retrospective descriptive analysis of the contributing factors using an in-house coding system and independent manual review of the cases by two registered nurses. Fifty-three cases were identified. Twenty-six cases originated from thermal sources (49.1%), 16 from fires (30.2%), 5 from chemical sources (9.4%), and 6 from undetermined sources (11.3%). Burns affected <10% total body surface area (TBSA) in 62.3% of cases, 10-19% in 3.8%, or unspecified TBSA in 34.0%. Approximately 90% involved the head/neck (49.1%) or trunk (41.5%). Common contributing factors were improper use or malfunction of devices such as cautery resulting in thermal burns, oxygen concentration above the lowest level during electrocautery causing fire, and incorrect application of antiseptic agents during skin preparation leading to chemical burns. Non-technical factors also contributed, such as failure of surgical teams to communicate critical information intra-operatively. These results demonstrate that it may be beneficial to focus on strategies to improve situational awareness, including team communication, and adherence to surgical safety protocols and policies in order to mitigate surgical burns or fires. While infrequent, these events provide opportunities to improve surgical safety. Effective team communication and system strategies to manage the fire triangle (ignition, oxygen, fuel) may help prevent these “never 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".