Diagnostic point-of-care ultrasound in obstetric anesthesia and critical care: a scoping review protocol
Bibliographic record
Abstract
Abstract Background Point-of-care ultrasound (POCUS) has gained popularity as a bedside diagnostic imaging modality. In obstetrical populations, particularly in acute care settings, POCUS serves as a valuable complement to clinical assessment. Despite its many applications, only a few have been defined and validated in the obstetric population. This scoping review aims to delineate literature on the diagnostic applications of POCUS in obstetric anesthesia and critical care. Methods This review will adhere to the Joanna Briggs Institute methodology for scoping reviews, as updated by Arksey and O’Malley and in stages elaborated by Levac et al. Relevant literature will be identified using Medical Subject Headings (MeSH), keyword, and proximity searches and combined using Boolean operators in PubMed, Embase, and Web of Science from January 1, 2000, to the present. Two independent reviewers will screen literature against predefined eligibility criteria in abstract and full-text forms. A third reviewer will be consulted if consensus cannot be reached. Data extraction will be systematic, focusing on pre-specified variables aligned with the review’s aims. Descriptive statistical and thematic analysis will follow data extraction, with findings presented in graphical and tabular forms. The reporting will follow Preferred Reporting Items for Systematic reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR). Conclusion This review will present the scope of the current literature on diagnostic POCUS in obstetric anesthesia and critical care, highlighting both strengths and gaps in existing knowledge. The insights gained will support future research, knowledge synthesis, and development of educational programs. The findings will be disseminated through peer-reviewed journal publications, conferences, and social media platforms. Systematic review registration Not applicable.
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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 | 0.104 | 0.101 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.015 | 0.012 |
| Bibliometrics | 0.026 | 0.020 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.065 | 0.015 |
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".