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Record W1969038481 · doi:10.1186/cc13153

Diagnosis and management of temperature abnormality in ICUs: a EUROBACT investigators' survey

2013· article· en· W1969038481 on OpenAlexaff
Daniel J. Niven, Kevin B. Laupland, Alexis Tabah, Aurélien Vesin, Jordi Rello, Despoina Koulenti, George Dimοpoulos, Jan J. De Waele, Jean‐François Timsit

Bibliographic record

VenueCritical Care · 2013
Typearticle
Languageen
FieldMedicine
TopicThermal Regulation in Medicine
Canadian institutionsRoyal Inland HospitalUniversity of Calgary
FundersBeijing Friendship Hospital, Capital Medical UniversityEuropean Society of Intensive Care MedicineDalian Medical UniversityChina Medical UniversityUniversitair Ziekenhuis GentSun Yat-sen UniversitySichuan UniversityWest China Hospital, Sichuan UniversitySoutheast UniversityGuangxi Medical UniversityCapital Medical UniversitySveučilište u Zagrebu
KeywordsMedicineIntensive careEmergency medicineQuartileIntensive care medicineInternal medicineConfidence interval

Abstract

fetched live from OpenAlex

INTRODUCTION: Although fever and hypothermia are common abnormal physical signs observed in patients admitted to intensive care units (ICU), little data exist on their optimal management. The objective of this study was to describe contemporary practices and determinants of management of temperature abnormalities among patients admitted to ICUs. METHODS: Site leaders of the multi-national EUROBACT study were surveyed regarding diagnosis and management of temperature abnormalities among patients admitted to their ICUs. RESULTS: Of the 162 ICUs originally included in EUROBACT, responses were received from 139 (86%) centers in 23 countries in Europe (117), South America (8), Asia (5), North America (4), Australia (3) and Africa (2). A total of 117 (84%) respondents reported use of a specific temperature threshold in their ICU to define fever. A total of 14 different discrete levels were reported with a median of 38.2°C (inter-quartile range, IQR, 38.0°C to 38.5°C). The use of thermometers was protocolized in 91 (65%) ICUs and a wide range of methods were reportedly used, with axillary, tympanic and urinary bladder sites as the most common as primary modalities. Only 31 (22%) of respondents indicated that there was a formal written protocol for temperature control among febrile patients in their ICUs. In most or all cases practice was to control temperature, to use acetaminophen, and to perform a full septic workup in febrile patients and that this was usually directed by physician order. While reported practice was to treat nearly all patients with neurological impairment and most patients with acute coronary syndromes and infections, severe sepsis and septic shock, this was not the case for most patients with liver failure and fever. CONCLUSIONS: A wide range of definitions and management practices were reported regarding temperature abnormalities in the critically ill. Documenting temperature abnormality management practices, including variability in clinical care, is important to inform planning of future studies designed to optimize infection and temperature management strategies in the critically ill.

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)0.003
metaresearch head score (Gemma)0.008
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 score0.003
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.327
Teacher spread0.294 · 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".

Quick stats

Citations49
Published2013
Admission routes1
Has abstractyes

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