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Color Flow Doppler in Spinal Ultrasound: A Novel Technique for Assessment of Catheter Position in Labor Epidurals

2023· article· en· W4386350172 on OpenAlexaff
Oscar F. C. van den Bosch, Yehoshua Gleicher, Cristián Arzola, Naveed Siddiqui, Kristi Downey, Jose C. A. Carvalho

Bibliographic record

VenueObstetric Anesthesia Digest · 2023
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicineCatheterAnesthesiaSalineVisibilityUltrasoundSurgeryRadiology

Abstract

fetched live from OpenAlex

(Reg Anesth Pain Med 2022;47:775–779) Epidural analgesia is one of the most common pain relief treatments during labor and delivery and is usually very effective. In a small percentage of epidurals, misplacement of the epidural catheter can cause inadequate anesthetic effects. To reduce instances of insufficient pain relief, it would be beneficial to be able to assess the position and flow of the catheter before administering medication. This study was designed to visualize flow in the epidural space using a structured color flow Doppler assessment, and to describe the location of the flow relative to the catheter insertion site. The primary outcome was the visualization of flow in the epidural space by color flow Doppler ultrasound. Secondary outcomes were visualization of catheter flow at the interspace of insertion, and at the interspaces above and below insertion, as well as visibility from unilateral or bilateral view, and visibility of flow on injection of saline only or an injection of a saline and air mixture.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.029
GPT teacher head0.310
Teacher spread0.281 · 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 designBench or experimental
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
Published2023
Admission routes1
Has abstractyes

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