Thrombin generation, bleeding and hemostasis in humans: Protocol for a scoping review of the literature
Bibliographic record
Abstract
INTRODUCTION: Hemostasis and bleeding are difficult to measure. Thrombin generation assays (TGAs) can measure both procoagulant and anticoagulant contributions to coagulation. TGAs might prove useful for the study of bleeding disorders. There has been much progress in TGA methodology over the past two decades, but its clinical significance is uncertain. We will undertake a scoping review of the literature to synthesize available information on the application of TGAs towards the study of bleeding and hemostasis, TGA methodologies being used and to summarize available literature on associations between TGA parameters, bleeding and hemostatic outcomes. METHODS AND ANALYSIS: MEDLINE, EMBASE and the Cochrane Central Register of Controlled Trials (CENTRAL) will be searched in collaboration with an information specialist. Title/abstract and full-text screening will be carried out independently and in duplicate; eligible study types will include randomized controlled trials, non-randomized studies, systematic reviews, and case series reporting TGA results and bleeding/hemostatic outcomes among humans. Mapping the information identified will be carried out with results presented using qualitative data analytical techniques. ETHICS AND DISSEMINATION: This scoping review will use published, publicly available information. Research ethics approval will not be required. We will disseminate our findings using conference presentations, peer-reviewed publications, social media, and engagement with knowledge users. This review will outline knowledge gaps concerning TGAs, better delineate its applicability as a clinically relevant assay for bleeding. and seek to identify ongoing barriers to its widespread adoption in clinical research, and eventually, in the clinical setting. TRAIL REGULATIONS: Registration ID with Open Science Framework: osf.io/zp4ge.
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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 | 0.101 | 0.096 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.016 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.092 | 0.019 |
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".