Methodologic issues in the use of bleeding as an outcome in transfusion medicine studies
Bibliographic record
Abstract
BACKGROUND: Prophylactic platelet transfusions are given to thrombocytopenic patients to prevent bleeding. The benefit of platelet transfusions has frequently been assessed by measuring the count increment; however, more recently, an assessment of bleeding has been used because it is a more clinically relevant outcome measure. The purpose of this study was to identify platelet transfusion trigger studies that used bleeding as an outcome measure, compare and contrast methods used to document bleeding and analyze bleeding outcomes, and identify and discuss methodologic issues to consider when bleeding is used as a study outcome. STUDY DESIGN AND METHODS: A systematic search to identify platelet transfusion trigger studies was performed. Relevant articles were reviewed to identify how bleeding data was captured and analyzed, and methodologic considerations were identified. RESULTS: Seven articles meeting the predefined entry criteria were identified. Methods used to document bleeding included chart review and clinical assessment. The frequency of assessment and the type of personnel performing the assessment were variable. Four approaches to analysis were identified: descriptive; comparison of the proportions of patients having at least one bleed; comparison of patient days with bleeding expressed as a proportion of the total days at risk of bleeding; and time-to-event (first bleed) analysis. CONCLUSION: Methodologic issues for consideration when designing a clinical study with bleeding as the outcome measure included approaches to minimize bias in the documentation and classification of bleeding and selection of an analysis approach that is appropriate to the question being asked. The need for development of a valid and reliable bleeding scale was also identified.
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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 | 0.793 | 0.885 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.043 | 0.047 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.011 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".