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Programmed Intermittent Epidural Bolus for Labor Analgesia: A Randomized Controlled Trial Comparing Bolus Delivery Speeds of 125 mL/hr Versus 250 mL/hr

2022· article· en· W4310552040 on OpenAlexaff
Y. Mazda, C. Arzola, K. Downey, Y. Xiang, J. Carvalho

Bibliographic record

VenueObstetric Anesthesia Digest · 2022
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicineBolus (digestion)AnesthesiaLocal anestheticDemographicsEpidural blockAnestheticRandomized controlled trialSurgery

Abstract

fetched live from OpenAlex

(Can J Anaesth. 2022;69:86–96) Previous studies have shown that programmed intermittent epidural bolus, the automatic delivery of boluses at certain times during epidural analgesia, decreases pain, motor block, and local anesthetic consumption while increasing patient satisfaction. However, unnecessarily high sensory levels have been noted in prior studies. Variables such as patient demographics and characteristics, patient positioning, medication volume, local anesthetic concentration, and injection level have all been researched in relation to epidural spread. The impact of delivery rates on sensory block has not been extensively researched. The aim of this study was to compare injection speeds of 250 mL/h and 125 mL/h administered at 40-minute intervals during the first stage of labor. The authors hypothesized lower delivery speeds would reduce sensory block levels.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.027
GPT teacher head0.270
Teacher spread0.243 · 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 designRandomized trial
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

Citations0
Published2022
Admission routes1
Has abstractyes

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