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Record W2418217153

[Efficacy and complications of fentanyl intravenous infusions in postoperative pediatric patients].

2008· article· en· W2418217153 on OpenAlexaboutno aff
Yuka Kurihara, Tetsuro Kagawa, Takeshi Suzuki, Hiroyasu Ohnishi, Noriyuki Ikeshima

Bibliographic record

VenuePubMed · 2008
Typearticle
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFentanylAnesthesiaSedationVomitingLaparotomyNauseaIncidence (geometry)Adverse effectSurgeryInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of the study was to evaluate the efficacy and the incidence of complication in pediatric patients for laparotomy receiving continuous fentanyl infusion for postoperative pain. METHODS: We treated 21 children, including 9 male and 12 female, aged 1-4 years old with the median age 2.0 +/- 1.0 years. They received postoperative intravenous fentanyl infusion 1 microg +/- kg(-1) x hr(-1) for about 50 hrs. We assessed the level of pain by Children's Hospital of Eastern Ontario pain scale (CHEOPS), and evaluated the additional medication of analgetics and the adverse events such as vomiting, the decrease of respiratory rate or Sp(O2) depression defined as the need for supplemental oxygen to maintain Sp(O2) > 95% and sedation by visiting the patients twice par day. RESULTS: Adequate analgesia occurred in over 90% of patients with the average CHEOPS score of 6.4 +/- 0.2 points. The incidences of vomiting and deep sedation were 14.3% and 19.0%, respectively, but there was no incidence of desaturations and decrease of respiratory rate, and we have no need to ensure emergency airway patency. CONCLUSIONS: Intravenous fentanyl infusion for postoperative pain in pediatric patients after laparotomy is an effective and safe procedure with a few complications.

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)0.002
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.027
GPT teacher head0.224
Teacher spread0.197 · 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

Citations2
Published2008
Admission routes1
Has abstractyes

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