MétaCan
Menu
Back to cohort
Record W4291162713 · doi:10.1097/shk.0000000000001965

EVALUATING SEPSIS CRITERIA IN DETECTING ALTERATIONS IN CLINICAL, METABOLIC, AND INFLAMMATORY PARAMETERS IN BURN PATIENTS

2022· article· en· W4291162713 on OpenAlexaff
Carly M. Knuth, Sarah Rehou, Dalia Barayan, Marc G. Jeschke

Bibliographic record

VenueShock · 2022
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsMcMaster UniversityUniversity of TorontoHamilton Health SciencesSunnybrook HospitalHealth Sciences CentreSunnybrook Health Science Centre
FundersNational Institute of General Medical SciencesNational Institute on Aging
KeywordsSepsisMedicineIntensive care medicineBurn injurySeptic shockBurn centerOrgan dysfunctionSystemic inflammatory response syndromeInternal medicineEmergency medicineSurgeryPoison control

Abstract

fetched live from OpenAlex

ABSTRACT: Sepsis has become the leading cause of death in burn patients. Furthermore, sepsis and septic complications result in significant morbidities and longer hospitalization, which has profound impacts on the healthcare system. Despite this, sepsis in burn patients is surprisingly poorly understood and characterized. This retrospective, single-institution cohort study aimed to increase our understanding of the septic response after burns. We hypothesized that different sepsis definitions will results in distinctive septic trajectories and biochemical patterns after injury. Sepsis was defined by our burn center-specific prospective definition, the American Burn Association criteria, Sepsis-3 criteria, and the Mann-Salinas criteria. Applying these definitions, we compared clinical, metabolic, and inflammatory markers in septic and nonseptic burn patients. We found that the Sepsis-3 criteria are the most reliable screening tool used before clinical diagnoses for detecting sepsis trajectories and biochemical patterns. Moreover, we characterized distinct temporal alterations in biomarkers during the pre- and post-septic periods in burn patients, which may be incorporated into future sepsis definitions to improve the accuracy of a sepsis diagnosis in burn patients.

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 score0.023
Threshold uncertainty score0.423

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.075
GPT teacher head0.403
Teacher spread0.328 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueShockSame topicBurn Injury Management and OutcomesFrench-language works237,207