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Record W2596077673 · doi:10.1097/bcr.0000000000000513

Healthcare Resource Utilization Associated with Burns and Necrotizing Fasciitis

2017· article· en· W2596077673 on OpenAlexaff
Eileen Burnett, Justin Gawaziuk, Kevin Shek, Sarvesh Logsetty

Bibliographic record

VenueJournal of Burn Care & Research · 2017
Typearticle
Languageen
FieldMedicine
TopicStreptococcal Infections and Treatments
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineFasciitisCohortAmputationRetrospective cohort studyIntensive care unitReferralEmergency medicineBurn centerSurgeryHealth careSevere burnIntensive care medicineInternal medicinePoison control

Abstract

fetched live from OpenAlex

Necrotizing fasciitis (NF) patients are increasingly managed in burn units. Although treated similarly to burns, the healthcare resource utilization (HRU) in NF appears to be greater. Accurate knowledge of the HRU is important to better allocate resources and to compare outcomes between units. The goal of this study is to provide better understanding of the HRU for NF compared with burns. A retrospective chart review of patients ≥18 years admitted to two regional tertiary referral centers with either NF or burns requiring surgery. The authors examined age, sex, %TBSA, geographic region, anatomical location, length of stay (LOS) in hospital, LOS in intensive care unit, number of operative procedures, number of packed red blood cells transfused, amputation, death, and use of free tissue transfer or skin graft. There were 210 NF and 209 burn patients. The NF cohort had a smaller TBSA (3.3 vs 10.0%), longer LOS (20 vs 14 days), and consequently a longer LOS/%TBSA (6.0 vs 1.5 days). This difference persisted after adjusting for age. More of the NF cohort (44.8%) spent ≥1 day in the intensive care unit. The NF cohort also had more procedures (median 2 vs 1), required blood (46.2 vs 16.7%), died in hospital (13.3 vs 4.3%), had an amputation (12.4 vs 4.8%), or required free tissue transfer (7.6 vs 2.9%). This study shows that NF requires substantially more HRU compared with burns. This information is important in recognizing the impact of these patients on burn units and planning for allocation of appropriate resources.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.075
Threshold uncertainty score0.610

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.175
GPT teacher head0.457
Teacher spread0.282 · 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 teacher head, 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".

Quick stats

Citations20
Published2017
Admission routes1
Has abstractyes

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