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Efficacy of Intrathecal Fentanyl for Cesarean Delivery: A Systematic Review and Meta-analysis of Randomized Controlled Trials With Trial Sequential Analysis

2020· review· en· W4248849735 on OpenAlexaff
Uppal, S. Retter, M. Casey, S. Sancheti, K. Matheson, D.M. McKeen

Bibliographic record

VenueObstetric Anesthesia Digest · 2020
Typereview
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsNova Scotia Health AuthorityIzaak Walton Killam Health CentreDalhousie University
Fundersnot available
KeywordsMedicineFentanylAnesthesiaIntrathecalSpinal anesthesiaCesarean deliveryBupivacaineRandomized controlled trialMorphineSurgeryPregnancy

Abstract

fetched live from OpenAlex

(Anesth Analg. 2020;130:111–125) Cesarean delivery procedures have become more popular over the past few decades, and spinal anesthesia is a commonly used method of anesthesia for that operation. A majority of surveyed members of the Society for Obstetric Anesthesia and Perinatology stated that they preferred to administer hyperbaric 0.75% bupivacaine spinal anesthesia for cesarean deliveries, while a smaller percentage of anesthesiologists reported adding fentanyl, morphine, or a combination of both to an intrathecal bupivacaine solution. The addition of intrathecal opioids to spinal anesthesia medication has been reported to be effective for pain relief in some cases, but researchers are still unsure of the benefits, drawbacks, and optimal dosages of these medications. These investigators conducted their systematic review and meta-anlaysis to analyze the effectiveness of fentanyl both alone and in combination with morphine when added to intrathecal bupivacaine anesthesia during a cesarean delivery.

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)0.013
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.033
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0170.028
Bibliometrics0.0050.006
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.092
GPT teacher head0.349
Teacher spread0.256 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations6
Published2020
Admission routes1
Has abstractyes

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