A survey of current practices in the diagnosis of and interventions for inhalational injuries in Canadian burn centres
Bibliographic record
Abstract
OBJECTIVE: To summarize current Canadian practice patterns in the diagnosis of and interventions for inhalation injuries (INHI). METHODS: A 10-question survey regarding the diagnosis of and interventions for INHI was sent to the medical directors of all 16 burn centres across Canada. RESULTS: The response rate to the survey was 50%. Fibreoptic bronchoscopy is required for the diagnosis of INHI in only four centres (50%). The departments of intensive care, plastic surgery, otolaryngology and respirology are involved in performing fibreoptic bronchoscopy in 87.5%, 37.5%, 12.5% and 12.5% of Canadian burn centres, respectively. Intubation for INHI is most often based on physical examination results (87.5%) and clinical history (75%). The most common physical features believed to be most consistent with INHI are dyspnea (87.5%) and hoarseness (87.5%). Common treatments include intubation (87.5%), routine ventilatory support (87.5%) and chest physiotherapy (75%). None of the centres used nebulized heparin. A total of five centres (62.5%) routinely changed the fluid resuscitation protocol when INHI was diagnosed. Only two centres (25%) routinely used prophylactic antibiotics for INHI. CONCLUSION: Prospective, multicentre trials are needed to generate evidence-based consensus in the areas of diagnosis, grading and treatment for INHI in Canada.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".