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Are We Closer to Determining a Gold Standard for Sensory Block Testing During Labor Epidural Analgesia?

2025· article· en· W4407693621 on OpenAlexaff
Allana Munro, Vishal Uppal

Bibliographic record

VenueObstetric Anesthesia Digest · 2025
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsIzaak Walton Killam Health Centre
Fundersnot available
KeywordsMedicineGold standard (test)Epidural blockSensory systemBlock (permutation group theory)AnesthesiaNeuroscienceInternal medicine

Abstract

fetched live from OpenAlex

(Can J Anesth. 2024;71(6):720–726. doi:10.1007/s12630-023-02686-x) The use of an epidural during birth is a decision many women make to adequately reduce their pain. Clinicians use sensory block testing to ensure the anesthesia is appropriately delivered to the body in a manner that will adequately alleviate pain and not inhibit critical processes. Munro and Uppal comment on the recent publication by Casellato and colleagues, to review their protocol and findings while also reviewing the field generally.

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)0.198
metaresearch head score (Gemma)0.405
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.198
Threshold uncertainty score0.989

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1980.405
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0060.004
Science and technology studies0.0030.012
Scholarly communication0.0110.021
Open science0.0080.006
Research integrity0.0210.037
Insufficient payload (model declined to judge)0.0050.007

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 head0.034
GPT teacher head0.284
Teacher spread0.250 · 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.

Study designNot applicable
Domainnot available
GenreCommentary

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
Published2025
Admission routes1
Has abstractyes

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