Surgical Fires and Burns: A 5-Year Analysis of Medico-legal Cases
Bibliographic record
Abstract
Surgical fires and unintended intraoperative burns cause serious patient harm, yet surveillance data are lacking in Canada. Medico-legal data provide unique descriptions of these events which can inform burn prevention strategies. We extracted 5 years of data on closed (2012-2016) medico-legal cases involving surgical fires and burns from the database of our organization which, in 2016, provided medico-legal support to >93,000 Canadian physicians. We performed a retrospective descriptive analysis of contributing factors using an in-house coding system and case reviews. We identified 53 eligible burn cases: 26 from thermal sources (49.1%), 16 from fires (30.2%), 5 from chemical sources (9.4%), and 6 from undetermined sources (11.3%). Common burn sources were electrosurgical equipment, lasers, lighting, and improper temperatures (causing thermal burns), cautery or lasers combined with supplemental oxygen and/or a flammable fuel source (causing fire), and improperly applied solutions including antiseptics (causing chemical burns). Nontechnical factors also contributed to patient outcomes, such as nonadherence to protocols (15 cases, 28.3%), failures in surgical team communication (3 cases, 5.7%), and lost situational awareness leading to delays in recognizing and treating burns (7 cases, 13.2%). This retrospective study highlights a need for improved surgical safety interventions to address surgical fires and burns. These interventions could include: effectively implemented surgical safety protocols, surgical team communication strategies, and raising awareness about preventing, diagnosing, and managing surgical burns.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".