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Record W4394930631 · doi:10.1093/jbcr/irae036.243

610 From Flames to Facts: Unveiling Discrepancies in Burn Patient Documentation

2024· article· en· W4394930631 on OpenAlexaff
Matthew Boroditsky, Jenny B. Xiao, Fagun Jain, Sabina Dobrer, Erik Vu, Anthony Papp

Bibliographic record

VenueJournal of Burn Care & Research · 2024
Typearticle
Languageen
FieldMedicine
TopicPalliative Care and End-of-Life Issues
Canadian institutionsVancouver General HospitalUniversity of British ColumbiaUniversity of British Columbia Hospital
Fundersnot available
KeywordsMedicineDocumentationBurn unitsMedical emergencyEmergency medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.035
metaresearch head score (Gemma)0.193
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.965
Threshold uncertainty score0.186

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.193
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.006
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.162
GPT teacher head0.518
Teacher spread0.356 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designObservational
DomainReporting
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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