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Record W2130351340 · doi:10.1177/0885066611402463

Fever in the Critically Ill

2011· review· en· W2130351340 on OpenAlex
Daniel J. Niven, Caroline Léger, Henry T. Stelfox, Kevin B. Laupland

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Intensive Care Medicine · 2011
Typereview
Languageen
FieldMedicine
TopicThermal Regulation in Medicine
Canadian institutionsUniversity of CalgaryAlberta Health Services
Fundersnot available
KeywordsMedicineIntensive care medicineEpidemiologyIntensive careCritically illEtiologyRandomized controlled trialClinical trialInternal medicine

Abstract

fetched live from OpenAlex

Fever is common among patients admitted to intensive care units (ICUs). In spite of the frequency of its occurrence, the biological mechanisms regulating the initiation and progression of fever are poorly understood. In addition, there are few large studies reporting on the epidemiology and etiology of fever in general medical and surgical ICU patients. Current evidence suggests that the development of high fever by patients admitted to ICUs with a medical admission diagnosis is associated with an increased risk of death. The decision to treat fever should therefore be obvious, but several lines of evidence argue against temperature-lowering strategies. Furthermore, the use of different temperature control strategies in febrile patients without acute brain injury or acute myocardial infarction is guided by a paucity of randomized clinical trials and by a lack of understanding of the biology of the induction and control of fever. As such, a review of the epidemiology, molecular mechanisms, and immunology of fever as well as the evidence behind management of fever in the critically ill is pertinent to all critical care practitioners.

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.

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)0.038
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.852
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.038
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.083
GPT teacher head0.402
Teacher spread0.319 · 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