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Record W2067329010 · doi:10.1542/peds.110.3.553

Comparison of Oral Versus Normal and High-Dose Rectal Acetaminophen in the Treatment of Febrile Children

2002· article· en· W2067329010 on OpenAlexaff
Dennis Scolnik, Eran Kozer, Sheila Jacobson, Shelley Diamond, Nancy L. Young

Bibliographic record

VenuePEDIATRICS · 2002
Typearticle
Languageen
FieldMedicine
TopicThermal Regulation in Medicine
Canadian institutionsHospital for Sick ChildrenUniversity of TorontoSickKids Foundation
Fundersnot available
KeywordsMedicineAcetaminophenSuppositoryRectal administrationRectal temperatureDoseAnesthesiaSignificant differenceAntipyreticPlaceboInternal medicineAnalgesicPharmacology

Abstract

fetched live from OpenAlex

OBJECTIVES: To compare the defervescent effect of high-dose rectal suppository acetaminophen with the recommended oral and rectal dosages and to evaluate acceptability of rectal acetaminophen. METHODS: A randomized, controlled trial was performed in 70 patients aged 6 months to 6 years with fever > or =39 degrees C. Group A received rectal acetaminophen 15 mg/kg, group B received rectal acetaminophen 30 mg/kg, and group C received oral acetaminophen 15 mg/kg. Primary outcome was maximal change in temperature during the 3-hour study period after initial treatment. RESULTS: There were 24 patients in group A, 23 in group B, and 23 in group C. There was no significant difference in temperature change between the groups during the 3 hours or in the maximum drop in temperature or final temperature. Visual analog scores for satisfaction of parents did not reveal any significant differences between the oral and rectal routes. CONCLUSIONS: There was no difference between the temperature decrement in patients treated with 15 mg/kg oral acetaminophen and the same or double dose rectally. Thus, there seems to be no evidence to support the use of higher doses of rectal acetaminophen for the treatment of fever in children. The rectal route proved to be as acceptable as the oral among parents.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.021
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.041
GPT teacher head0.315
Teacher spread0.274 · 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

Citations54
Published2002
Admission routes1
Has abstractyes

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