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Record W4390724166 · doi:10.7759/cureus.51943

Antipyretic Use in Noncritically Ill Patients With Fever: A Review

2024· review· en· W4390724166 on OpenAlexaff
Khawar Tariq Mehmood, Shahad Al-Baldawi, Gabriel Zúñiga Salazar, Diego Zúñiga, Sneha Balasubramanian

Bibliographic record

VenueCureus · 2024
Typereview
Languageen
FieldMedicine
TopicThermal Regulation in Medicine
Canadian institutionsASTER
Fundersnot available
KeywordsAntipyreticMedicineAcetaminophenIntensive care medicineModalitiesPharmacologyAnalgesic

Abstract

fetched live from OpenAlex

Antipyretics are one of the most frequently used agents in medicine. Numerous pharmacological agents, such as acetaminophen, non-steroidal anti-inflammatory agents (NSAIDs), salicylates, and selective cyclooxygenase 2 (COX-2) inhibitors, and nonpharmacological treatment modalities, such as tepid sponging and cooling blankets, are available for temperature reduction. There is a scarcity of definitive clinical guidelines on the choice of various agents in noncritically ill febrile patients. Our review examined the various modalities available for antipyresis and compared their safety and efficacy. The rationale for the choice of a particular pharmacological agent and route of administration were scrutinized. Our review also envisaged the perceived beneficial effects of antipyretics against the harmful side effects, including the evaluation of morbidity or mortality advantage conferred by antipyretics. The various toxicities associated with these agents were also highlighted.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
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 head0.046
GPT teacher head0.367
Teacher spread0.321 · 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 designSystematic review
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

Citations6
Published2024
Admission routes1
Has abstractyes

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