Reliability of pressure waveform analysis to determine correct epidural needle placement in labouring women
Bibliographic record
Abstract
Summary Pressure waveform analysis provides a reliable confirmatory adjunct to the loss‐of‐resistance technique to identify the epidural space during thoracic epidural anaesthesia, but its role remains controversial in lumbar epidural analgesia during labour. We performed an observational study in 100 labouring women of the sensitivity and specificity of waveform analysis to determine the correct location of the epidural needle. After obtaining loss‐of‐resistance, the anaesthetist injected 5 ml saline through the epidural needle (accounting for the volume already used in the loss‐of‐resistance). Sterile extension tubing, connected to a pressure transducer, was attached to the needle. An investigator determined the presence or absence of a pulsatile waveform, synchronised with the heart rate, on a monitor screen that was not in the view of the anaesthetist or the parturient. A bolus of 4 ml lidocaine 2% with adrenaline 5 μg.ml −1 was administered, and the epidural block was assessed after 15 min. Three women displayed no sensory block at 15 min. The results showed: epidural block present, epidural waveform present 93; epidural block absent, epidural waveform absent 2; epidural block present, epidural waveform absent 4; epidural block absent, epidural waveform present 1. Compared with the use of a local anaesthetic bolus to ascertain the epidural space, the sensitivity, specificity, positive and negative predictive values of waveform analysis were 95.9%, 66.7%, 98.9% and 33.3%, respectively. Epidural waveform analysis provides a simple adjunct to loss‐of‐resistance for confirming needle placement during performance of obstetric epidurals, however, further studies are required before its routine implementation in clinical practice.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".