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Record W2021775548 · doi:10.1097/ccm.0b013e3181aa6117

Fever in the critically ill medical patient

2009· review· en· W2021775548 on OpenAlexaff
Kevin B. Laupland

Bibliographic record

VenueCritical Care Medicine · 2009
Typereview
Languageen
FieldMedicine
TopicThermal Regulation in Medicine
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineAntipyreticIntensive care medicineCritically illIntensive careClinical trialCritical illnessInternal medicineAnesthesia

Abstract

fetched live from OpenAlex

Fever, commonly defined by a temperature of >or=38.3 degrees C (101 degrees F), occurs in approximately one half of patients admitted to intensive care units. Fever may be attributed to both infectious and noninfectious causes, and its development in critically ill adult medical patients is associated with an increased risk for death. Although it is widespread and clinically accepted practice to therapeutically lower temperature in patients with hyperthermic syndromes, patients with marked hyperpyrexia, and selected populations such as those with neurologic impairment, it is controversial whether most medical patients with moderate degrees of fever should be treated with antipyretic or direct cooling therapies. Although treatment of fever may improve patient comfort and reduce metabolic demand, fever is a normal adaptive response to infection and its suppression is potentially harmful. Clinical trials specifically comparing fever management strategies in neurologically intact critically ill medical patients are needed.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.049
GPT teacher head0.420
Teacher spread0.371 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations146
Published2009
Admission routes1
Has abstractyes

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