Point-of-care platelet function testing for guided transfusion in neurosurgical management of intracranial hemorrhage: a systematic review
Bibliographic record
Abstract
Given the rising prevalence of antiplatelet therapy, rapid preoperative identification of patients with bleeding diathesis is necessary for the guidance of blood product administration. This is especially relevant in neurosurgery for intracranial hemorrhage (ICH), where indiscriminate transfusions may lead to further hemorrhagic or thromboembolic injury. Point-of-care (POC) testing of platelet function is a promising solution to this dilemma, as it has been proven effective in cardiac surgery. However, to date, POC platelet function testing in neurosurgery has not been extensively evaluated. This systematic review appraises the use of POC platelet function test (PFT) in emergency neurosurgery in terms of its impact on patient outcomes.A comprehensive search was conducted on four electronic databases (Pubmed, MEDLINE, Embase, and Cochrane) for relevant English language articles from their respective inceptions until 1 June 2022. We included all randomized controlled trials and cohort studies that met the following inclusion criteria: (i) involved adult patients undergoing neurosurgery for ICH; (ii) evaluated platelet function via POC PFT; (iii) reported a change in perioperative blood loss; and/or (iv) reported data on treatment-related adverse events and mortality. Assessment of study quality was conducted using the Newcastle Ottawa Quality Assessment Scale for Cohort Studies and Case-Control Studies, and the JBI Critical Appraisal Checklist for Case Series.The search yielded 2,835 studies, of which seven observational studies comprising 849 patients met the inclusion criteria for this review. Overall, there is evidence that the use of POC PFT to assess bleeding risk reduced bleeding events, thromboembolic adverse outcomes, and the length of hospitalization. However, there is currently insufficient evidence to suggest that using POC PFT improves blood product use, functional outcomes or mortality.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".