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Record W4221135551 · doi:10.1093/jbcr/irac012.122

120 The Association of Admission Cultures with Burn Outcomes

2022· article· en· W4221135551 on OpenAlexaff
Maree Baird, Joyce E Wall, Angela Man, Najib M Allabadi, Marc G. Jeschke, Alisa Savetamal, John Schulz, Kathleen S Romanowski

Bibliographic record

VenueJournal of Burn Care & Research · 2022
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsTD Bank Group
Fundersnot available
KeywordsMedicineTotal body surface areaInterquartile rangeBurn injuryBurn centerMedical recordPopulationRetrospective cohort studyBody surface areaInternal medicineEmergency medicineSurgeryPoison control

Abstract

fetched live from OpenAlex

Abstract Introduction Burn patients are susceptible to infections. It is thought that burn wounds are initially sterile and become colonized by commensal and environmental microorganisms. Many burn centers have protocols to routinely screen patients for infection on admission. The ability of culture results to predict outcomes in burn patients has not been examined. In this study, we aim to examine the relationship between admission cultures and burn outcomes. We hypothesize that patients who have positive cultures on admission will have increased mortality and length of stay (LOS). Methods A retrospective chart review was conducted using electronic medical records for all adult patients admitted to three ABA verified burn centers from January 2016 -December 2017. Data collected included patient demographics, burn injury, burn outcomes, and cultures obtained within the first 24 hours of admission. Data analysis was conducted using Chi-square, Fisher Exact, Spearman Correlation, Wilcoxon 2-sample, and Kruskal-Wallis tests. Results A total of 1615 patients (mean age 45.87±17.65 years, 1145 males [70.9%]) were analyzed. Mean total body surface area burn (TBSA) was 9.6±14.2% and 10% had inhalation injuries. In this study population, the median LOS was 7 days (Interquartile range [IQR] = 12) and 72 patients (4.5%) expired. Older patients (p < .0001), those with higher scores on the 11-factor modified frailty index (mFI-11) (p < 0.0001), a higher TBSA (p< .0.0001) and inhalation injury (p < 0.0001) had a higher mortality rate. In examining the effect of admission cultures on mortality, there was no significant difference in mortality based on wound culture (p 0.14), Clostridium difficile (C. diff) (p 0.25), or urine culture (p=0.79) results. Patients with positive Methicillin-Resistant Staphylococcus Aureus (MRSA) screening (p 0.04) and those with positive blood cultures (p 0.01) were more likely to die from their injuries. Older patients (r= 0.14, p < 0.0001), those with a larger TBSA (r=0.49, p < 0.0001), and a higher MFI-11 score (r=0.12, p < 0.0001) had and increased LOS.. There was no association between LOS and positive wound cultures (p 0.08), or blood cultures (p 0.49) upon admission. Patients with positive MRSA results (p 0.003) and urine cultures (p 0.01) upon admission had a longer LOS while those with positive C. diff results had a shorter LOS (p 0.01). Conclusions Mortality is associated with standard predictors of outcomes (age, burn size, inhalation injuries, frailty scores) and positive MRSA screens and blood cultures. Patients with larger burns (define larger burn-maybe use the degree scale), a positive MRSA and negative C. diff had a longer LOS. Based on these results, cultures should be considered in all patients upon admission to the hospital as they are predictive of burn outcomes.

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)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.042
GPT teacher head0.397
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
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".

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Citations0
Published2022
Admission routes1
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