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Reliability of Pressure Waveform Analysis to Determine Correct Epidural Needle Placement in Laboring Women

2018· article· en· W2803485267 on OpenAlexaff
Simone Derzi, Alan Moore, María Francisca Elgueta, M. Moustafa, Thomas Schricker, De Q.H. Tran

Bibliographic record

VenueObstetric Anesthesia Digest · 2018
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsMcGill University Health CentreMontreal General Hospital
Fundersnot available
KeywordsMedicineEpidural spaceWaveformLumbarAnesthesiaReliability (semiconductor)SurgeryComputer science

Abstract

fetched live from OpenAlex

( Anaesthesia . 2017;72(7):840–844) Epidural failure is a significant problem in obstetric anaesthesia with one survey of parturients reporting a failure rate of 23%. There can be many causes of epidural failure, including technical failure due to misidentification of the epidural space. A false loss-of-resistance during epidural needle advancement is a common cause of misidentification. Pressure waveform analysis has been used to provide confirmation of correct epidural needle position when the loss-of-resistance technique was used to identify the epidural space during thoracic epidural procedures. Accurate needle placement is confirmed if a pulsatile waveform synchronized with arterial pulsations is seen. In one study, the failure rate decreased from 24% with loss-of-resistance only to 2% with the addition of pressure waveform analysis. However, the reliability of this technique for lumbar epidural analgesia for labor is unclear. This observational study was performed to determine the reliability of pressure waveform analysis in identifying accurate epidural needle placement during lumbar epidural insertion for labor analgesia.

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 score0.047
Threshold uncertainty score0.928

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
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 head0.014
GPT teacher head0.251
Teacher spread0.237 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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