Snapshot of Obstetric National Audit and Research Project (SONAR1): aprotocol for an international observational cohort study
Bibliographic record
Abstract
INTRODUCTION: Caesarean birth (CB) under neuraxial anaesthesia (NA) is the most performed inpatient operation in the UK. The incidence of intraoperative pain during caesarean delivery performed under neuraxial anaesthesia is unclear, with limited data that used patient-reported measures to investigate intraoperative pain. The short- and medium-term impacts on patients of this adverse event are unknown. METHODS AND ANALYSIS: We will undertake a multicentre, prospective observational cohort study to evaluate the incidence and impact of pain experienced by patients during CB performed under neuraxial anaesthesia. Routine audit data will be collected for all patients undergoing caesarean delivery for any indication during a 1 week window at participating hospitals within the UK and Queensland, Australia. The dataset will include patient, anaesthetic, obstetric and neonatal risk factors for intraoperative pain. Local investigators will then seek informed consent from patients either before or within 24 hours of delivery to record patient experience and patient-reported outcomes at 24 hours and 6 weeks postdelivery. Local investigators at participating hospitals will also complete a survey evaluating compliance with evidence-based structural standards at their sites. The patient characteristics, structures, processes and outcomes will be described. Inferential techniques will be used to evaluate the relationship between risk factors and postoperative outcomes. ETHICS AND DISSEMINATION: This study received ethical approval from the Leicester Health Research Authority and Care Research Wales, REC reference 24/EM/0084) on 24 May 24. The study received ethical approval from the Human Research Ethics Committee of Metro North Health in Australia on 25 March 2024 (REC Ref HREC/2024/MNHA/103767). The results of the study will be reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology statement. The results will be disseminated via conference presentations, peer-reviewed academic journals and reports prepared for patients, the public and policy makers. TRIAL REGISTRATION NUMBER: ISRCTN15269213.
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".