Proportional Assist Ventilation for Minimizing the Duration of Mechanical Ventilation (the PROMIZING study): update to the statistical analysis plan for a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: We previously published the protocol and statistical analysis plan for a randomized controlled trial of Proportional Assist Ventilation for Minimizing the Duration of Mechanical Ventilation: the PROMIZING study in Trials ( https://doi.org/10.1186/s13063-023-07163-w ). This update summarizes changes made to the statistical analysis plan for the trial since the publication of the original protocol and statistical analysis plan. METHODS/DESIGN: The Proportional Assist Ventilation for Minimizing the Duration of Mechanical Ventilation (PROMIZING) study is a multi-center, open-label, randomized controlled trial designed to determine if ventilation with proportional assist ventilation with load-adjustable gain factors will result in a shorter duration of time spent on mechanical ventilation compared to ventilation with pressure support ventilation for patients with acute respiratory failure. The statistical analysis plan for the trial was incorporated into the original publication of the protocol in Trials ( https://doi.org/10.1186/s13063-023-07163-w ) and was based on version 5.0 of the study protocol and version 1.0 of the statistical analysis plan (SAP), which included plans for both frequentist and Bayesian analyses. We have since updated the SAP to refine the Bayesian analysis plan, update the multistate model diagram, and include plans for a cluster analysis to determine if there is heterogeneity of treatment effect. This update summarizes the changes made and their rationale and provides a refined SAP for the PROMIZING trial with additional background information, in adherence with guidelines for the prospective reporting of SAPs for randomized controlled trials. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT02447692 prospectively registered May 19, 2015.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".