The influence of bleeding on trigger changes for platelet transfusion in patients with chemotherapy‐induced thrombocytopenia
Bibliographic record
Abstract
BACKGROUND: For patients with thrombocytopenia without bleeding risk factors, a platelet transfusion trigger of 10 × 10(9) /L is recommended. No studies have evaluated the clinicians' decision-making process leading to trigger changes. STUDY DESIGN AND METHODS: We report on the evaluation of trigger changes and the relation with bleeding. Eighty patients previously enrolled in the SPRINT trial represent the patient population for the current analysis. RESULTS: Seventy-four patients had a starting trigger of 10 × 10(9) /L. Only a minority of patients treated with chemotherapy alone (3/12, 25%) and autologous transplant (6/15, 40%) had a change in their trigger in contrast to the majority of allogeneic transplant (37/47, 79%; p = 0.001 and p = 0.009, respectively, when compared to allogeneic transplant group). Bleeding was the main reason reported by clinicians for a trigger change, but the occurrence of significant bleeding (Grade 2-4) was similar in patients with or without a trigger change (51 and 54%, p = 1.00). Clinicians were influenced by the bleeding system: grade 1 mucocutaneous bleeding leading to a trigger change was overrepresented (71% of cases), as was grade 2 genitourinary bleeding not leading to a trigger change (57% of cases). CONCLUSION: A universal trigger of 10 × 10(9) /L may not be maintained in a diverse population of patients with their respective bleeding risk factors. Because the trigger is changed often, it may not be as effective as previously believed.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".