Bibliographic record
Abstract
Platelet transfusions are a commonly used medical therapy to prevent bleeding (prophylactic use), or to treat patients who are actively bleeding (therapeutic use). The most frequent use of prophylactic platelet transfusions occurs in patients with chemotherapy induced thrombocytopenia, although prophylactic platelet transfusions are also used in other thrombocytopenic patient populations prior to a surgical intervention. Therapeutic platelet transfusions used by many different patient populations with acute hemorrhage including: medical and surgical patients; trauma patients, patients with intraventricular hemorrhage and gastrointestinal bleeds. Randomized controlled trials designed to determine the optimal trigger, optimal dose, efficacy of a therapeutic only platelet transfusion strategy, and efficacy of pathogen reduced platelets have contributed to an evidence based approach for platelet transfusions over the past 15 years. Although we have learned a lot from these clinical trials, generalizability is limited with most trials have been conducted in adults with chemotherapy induced thrombocytopenia. There is a paucity of evidence to inform transfusion therapy in other patient populations. Methodological challenges associated with many of these studies have hampered the overall acceptance of the results; hence, knowledge uptake has been slow raising the question: why evidence based platelet transfusion changes occur so slowing even when RCT data are available? The answer to this question is complex but may include: research design limitations; the lack of generalizability of data from hematology/oncology patients to other patient populations; and, methodological limitations including clinical relevance and/or challenges with measuring the outcome. Information generated through clinical research related to platelet transfusion has provided some guidance to inform the practice of evidence based platelet transfusion therapy; however, along that path we have also recognized the flaws and limitations of the clinical research methodology used which has limited practice change. The question for transfusion researchers today is –how do we move forward from here to ensure that research resources are best spent to inform evidence based practices that will benefit our patients? In this presentation what we know about evidence based transfusion practices will be reviewed with emphasis on the potential limitations associated with clinical research as explanations for the lack of practice change. Activities underway to overcome some of these limitations will also be discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.234 | 0.416 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.020 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".