Methods for determination of optimal positive end-expiratory pressure: a protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: Titrated application of positive end-expiratory pressure (PEEP) is an important part of any mechanical ventilation strategy. However, the method by which the optimal PEEP is determined and titrated varies widely. Methods for determining optimal PEEP have been assessed using a variety of different study designs and patient populations. We will conduct a scoping review to systematically identify all methods for determining optimal PEEP, and to identify the patient populations, outcomes measured and study designs used for each method. The goal will be to identify gaps in the optimal PEEP literature and identify areas where there may be an opportunity to further systematically synthesise and meta-analyse existing literature. METHODS AND ANALYSIS: Using scoping review methodology, we will generate a comprehensive search strategy based on inclusion and exclusion criteria generated using the population, concept, context framework. Five different databases will be searched (MEDLINE, EMBASE, CENTRAL, Web of Science and Scopus). Three investigators will independently screen titles and abstracts, and two investigators will independently complete full-text review and data extraction. Included citations will be categorised in terms of PEEP method, study design, patient population and outcomes measured. The methods for PEEP titration will be described in detail, including strengths and limitations. ETHICS AND DISSEMINATION: Given this is a synthesis of existing literature, ethics approval is not required. The results will be disseminated to stakeholders via presentation at local, regional and national levels, as well as publication in a high-impact critical care journal. There is also the potential to impact local clinical care protocols and inform broader clinical practice guidelines undertaken by societies.
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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 | 0.168 | 0.192 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.029 | 0.024 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.008 | 0.011 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.074 | 0.022 |
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".