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Record W2898261345 · doi:10.4293/jsls.2018.00023

Use of Paracervical Block Before Laparoscopic Supracervical Hysterectomy

2018· article· en· W2898261345 on OpenAlexaboutno aff
Rachel L. Barr Grzesh, Alejandro D. Treszezamsky, Suzanne S. Fenske, Lauren G. Rascoff, Erin Moshier, Charles Ascher‐Walsh

Bibliographic record

VenueJSLS Journal of the Society of Laparoscopic & Robotic Surgeons · 2018
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnesthesiaPlaceboBupivacaineParacervical blockHysterectomyRandomized controlled trialEpinephrineSalineAnalgesicSurgeryLidocaine

Abstract

fetched live from OpenAlex

Background and Objective: Some patients who undergo laparoscopic hysterectomy request overnight admission for pain management, thus increasing costs for a surgery that is safe for same-day discharge. We wanted to evaluate whether a paracervical block of bupivacaine with epinephrine before laparoscopic supracervical hysterectomy would decrease overnight admission rates, postoperative pain, and pain medication requirement Methods: This was a randomized, double-blind, placebocontrolled, parallel-group trial (Canadian Task Force classification I) at an academic medical center. Patients undergoing laparoscopic supracervical hysterectomy were randomized to a 20-mL paracervical injection of either 0.25% bupivacaine with epinephrine or 20 mL normal saline before skin incision. All providers, except the circulating nurse, were blinded. The primary outcome was overnight hospital admission rate. Secondary outcomes included postoperative pain medication use and pain scores. Analysis included t test, 2 , Wilcoxon, and ANOVA.

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 score0.016
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.037
GPT teacher head0.286
Teacher spread0.249 · 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

Citations13
Published2018
Admission routes1
Has abstractyes

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