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Chronopharmacology of Intrathecal Sufentanil for Labor Analgesia

2004· article· en· W1975526622 on OpenAlexaff
Richard Debon, Emmanuel Boselli, Romain Guyot, Bernard Allaouchiche, Björn Lemmer, D. Chassard

Bibliographic record

VenueAnesthesiology · 2004
Typearticle
Languageen
FieldMedicine
TopicPain Management and Opioid Use
Canadian institutionsHotel Dieu Hospital
Fundersnot available
KeywordsMedicineSufentanilIntrathecalAnesthesia

Abstract

fetched live from OpenAlex

BACKGROUND: The pharmacokinetic and pharmacodynamic characteristics of opioids vary throughout the day, as demonstrated for oral morphine in chronic pain. However, little is known about the chronobiology of intrathecal lipid soluble opioids used for labor analgesia. The aim of this prospective study was to determine whether the duration of action of intrathecally administered sufentanil is influenced by the time of administration. METHODS: Ninety-one women in the first stage of labor were enrolled. Labor analgesia was first provided by 10 microg intrathecal sufentanil. The duration of action of intrathecal sufentanil was measured and analyzed by the cosinor method to determine periodic intraday variation. RESULTS: Pain assessed by a visual analog score was not different among patients (70 +/- 17 mm) before the injection of intrathecal sufentanil. Rhythm analysis revealed a mean ( +/- SD) duration of analgesia (mesor) of 93.0 +/- 3.8 min. A highly significant 12-h rhythm was found, with two peaks: One was near midnight (0.78 +/- 0.6 h), and the other was near noon (12.78 +/- 0.6 min) (P < 0.01). The amplitude of this 12-h component was 16.1 +/- 5.5 min. CONCLUSIONS: The duration of intrathecal sufentanil analgesia exhibited a temporal pattern with 30% variations throughout the day period. The authors point out that the lack of consideration of chronobiological conditions in intrathecally administered analgesia studies can cause significant statistical bias. Further studies dealing with intrathecal opioids should consider the time of drug administration.

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 score0.261
Threshold uncertainty score0.326

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 head0.015
GPT teacher head0.295
Teacher spread0.280 · 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

Citations30
Published2004
Admission routes1
Has abstractyes

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