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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".