610 From Flames to Facts: Unveiling Discrepancies in Burn Patient Documentation
Bibliographic record
Abstract
Abstract Introduction Comprehensive and accurate burn documentation is essential for initial and ongoing patient care. However, the challenge of inaccurate and incomplete records poses preventable risks. Our study evaluates burn documentation at a tertiary care burn centre, focusing on discrepancies between initial Emergency Department (ED) assessments and final evaluations by the Plastic Surgery Burn Consultant (PS). We hypothesize that the ED reports inaccurate and incomplete burn injury details, leading to differences in burn size and severity compared to PS assessments. Methods We conducted a retrospective review of our provincial burn registry from January 1, 2016, to December 31, 2021. We included patients admitted for burns warranting a PS consultation, excluding isolated first-degree, ocular, and inhalational burns, and those not requiring burn unit admission. Data covering time, date, etiology, injury details, treatment, and follow-up were collected and compared between ED and PS records. Incomplete entries lacked burn-specific data points. Mann-Whitney and Kruskal-Wallis H tests were used to compare continuous outcomes, while Pearson’s Chi-Square test was employed for categorical outcomes. Wilcoxon’s Signed-Rank test was used to identify significant variations in TBSA estimates, with PS considered the "gold" standard. Statistical significance was set at p< 0.05. Results 358 patients were included, with most burns in male patients (76%) occurring at home and involving the head and neck. Burn etiology, circumstances, place, and anatomic location were well reported and consistent across PS and ED documentation. However, there were significant differences in TBSA estimates. The ED calculated a median TBSA of 20 (IQR: 19.8), while PS estimated a median TBSA of 14 (IQR: 16) (p< 0.0019). Notably, TBSA estimates for burns < 10% and 10-25% showed significant differences (p< 0.0001), tending toward overestimation by the ED. Deeper burns were consistently over-reported during initial ED assessments compared to the final PS determinations. Furthermore, 81% of the initial records were incomplete: 66% lacked initial treatment data, 49% missed TBSA, and 39% omitted burn depth. Conclusions Significant discrepancies were appreciated in the initial ED documentation of burn injuries at our tertiary care burn centre, with overestimations in TBSA and burn depth. Over 80% of initial documentation was incomplete, with TBSA omitted in 49% of charts in both local and peripheral transfer consultations. There is a collective urgent need for enhanced awareness and education on the importance of accurate and comprehensive burn patient documentation. Applicability of Research to Practice This research emphasizes the need for improved documentation strategies for burn patients seen in the acute care setting, raising the potential to venture into modalities such as artificial intelligence and electronic health record systems to enhance burn documentation.
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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 | 0.035 | 0.193 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".