Evaluation of platelet transfusion triggers in a tertiary‐care hospital
Bibliographic record
Abstract
BACKGROUND: Our 1100-bed referral hospital uses approximately 12,000 units of random-donor platelets (PLTs) and 1,900 units of single-donor apheresis PLTs per year with a mean of 23 percent outdating. An analysis of patterns of utilization has been undertaken to evaluate practice. STUDY DESIGN AND METHODS: Over a 9-month period, data were collected on a total of 1682 transfusion episodes in 464 patients. When the pretransfusion count was greater than 10 x 10(9) per L an attempt was made to identify the specific indications for PLT transfusions such as bleeding. RESULTS: The majority (78%) of PLTs were transfused when the counts were above 10 x 10(9) per L. The mean pretransfusion counts for different services were: bone marrow transplant (BMT) 17.4 x 10(9) per L, hematology-oncology 14.6 x 10(9) per L, the Heart Institute 3 x 10(9) per L, and other services 36 x 10(9) per L. The percentage of transfusions given to patients with a count greater than 10 x 10(9) per L varied by service with 79 percent in BMT, 60 percent in hematology and oncology, 98 percent at the Heart Institute, and 81 percent in other services. Routine monitoring of counts shows a mean increment of 10.2 x 10(9) per L per transfusion. One hour posttransfusion counts, 24-hour posttransfustion counts, and documentation of clinical justification for transfusions was often not available. CONCLUSIONS: The data show that most patients who receive PLTs have pretransfusion counts of more than 10 x 10(9) per L and more than one-third have pretransfusion counts of greater than 20 x 10(9) per L. The medical literature supports prophylactic PLT transfusion based solely on the count when the PLT number is 10 x 10(9) per L or less. Above this level additional justification is needed although there are different points of view concerning the appropriate triggers. Our data suggest that there is a need for clear hospital transfusion guidelines and ongoing monitoring of PLT use.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".