Defining clinically important perioperative blood loss and transfusion for the Standardised Endpoints for Perioperative Medicine (StEP) collaborative: a protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: 'Standardised Endpoints for Perioperative Medicine' (StEP) is an international collaboration undertaking development of consensus-based consistent definitions for endpoints in perioperative clinical trials. Inconsistency in endpoint definitions can make interpretation of trial results more difficult, especially if conflicting evidence is present. Furthermore, this inconsistency impedes evidence synthesis and meta-analyses. The goals of StEP are to harmonise definitions for clinically meaningful endpoints and specify standards for endpoint reporting in clinical trials. To help inform this endeavour, we aim to conduct a scoping review to systematically characterise the definitions of clinically important endpoints in the existing published literature on perioperative blood loss and transfusion. METHODS AND ANALYSIS: The scoping review will be conducted using the widely adopted framework developed by Arksey and O'Malley, with modifications from Levac. We refined our methods with guidance from research librarians as well as researchers and clinicians with content expertise. The electronic literature search will involve several databases including Medline, PubMed-not-Medline and Embase. Our review has three objectives, namely to (1) identify definitions of significant blood loss and transfusion used in previously published large perioperative randomised trials; (2) identify previously developed consensus-based definitions for significant blood loss and transfusion in perioperative medicine and related fields; and (3) describe the association between different magnitudes of blood loss and transfusion with postoperative outcomes. The multistage review process for each question will involve two reviewers screening abstracts, reading full-text articles and performing data extraction. The abstracted data will be organised and subsequently analysed in an iterative process. ETHICS AND DISSEMINATION: This scoping review of the previously published literature does not require research ethics approval. The results will be used to inform a consensus-based process to develop definitions of clinically important perioperative blood loss and transfusion. The results of the scoping review will be published in a peer-reviewed scientific journal.
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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 | 0.212 | 0.246 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.014 | 0.017 |
| Bibliometrics | 0.028 | 0.027 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.012 | 0.011 |
| Insufficient payload (model declined to judge) | 0.037 | 0.011 |
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".