A dose of platelets: getting it just right
Bibliographic record
Abstract
Abstract: Platelet transfusions are commonly administered to patients with thrombocytopenia, hemorrhage and platelet dysfunction. Although the optimal threshold for transfusion and the appropriate number of platelets per dose are not defined in all clinical situations, a platelet count of 10,000/µL is a generally accepted threshold for prophylactic transfusions in uncomplicated patients. Meanwhile, the minimum yield for collection or the size of a standard dose is higher in the United States under the Food and Drug Administration regulatory requirements than in European nations and in Canada. Given the challenges in maintaining donor recruitment and product supplies, minimal yield specifications have come under scrutiny, and utilization criteria for platelet products have come under investigation with evidence that many prophylactic platelet transfusions are administered to patients when their counts are above the recommended guideline threshold without evidence of bleeding. These findings suggest that there are opportunities to improve practices surrounding platelet collections and transfusion practices, reducing platelet dosages and number of transfusions administered, that would positively affect the availability of platelet products in the face of severe supply shortages and avoid platelet transfusions that provide little to no significant clinical benefit to patients and potentially cause more harm than good.
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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.027 | 0.017 |
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".