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Determination of the Optimal Programmed Intermittent Epidural Bolus Volume of Bupivacaine 0.0625% With Fentanyl 2 μg/mL at a Fixed Interval of 40 Minutes: A Biased Coin Up-and-down Sequential Allocation Trial

2019· article· en· W2913554945 on OpenAlexaff
P. Zakus, Cristián Arzola, Ricardo Bittencourt, Kristi Downey, X.Y. Ye, Jose C. A. Carvalho

Bibliographic record

VenueObstetric Anesthesia Digest · 2019
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicineFentanylBupivacaineAnesthesiaBolus (digestion)DosingRegimenLocal anestheticSurgeryPharmacology

Abstract

fetched live from OpenAlex

( Anaesthesia . 2018;73:459–465) The use of programmed intermittent epidural bolus (PIEB) to provide labor analgesia has been increasing in popularity as commercially available pumps have become available. A variety of dosing strategies have been evaluated but the optimal settings for PIEB have not yet been established. The majority of studies have included PIEB in conjunction with patient controlled epidural analgesia (PCEA). The current investigators had previously investigated which PIEB dosing strategy would minimize breakthrough pain and the need for PCEA boluses. They determined the optimal regimen was 10 mL of bupivacaine 0.0625% with fentanyl 2 µg/ml administered every 40 minutes. However, 34% of women exhibited a sensory block to ice above T6, which might indicate an unnecessarily high spread of local anesthetic. Therefore, they conducted this prospective, double-blind, dose-finding study to investigate whether it was possible to reduce the PIEB volume without compromising the efficacy of the technique.

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)0.001
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 score0.042
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.241
Teacher spread0.223 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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