The risk of bleeding in thrombocytopenic patients with acute myeloid leukemia.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Patients with acute myeloid leukemia are at risk of bleeding. The risk factors for different severities of bleeding are poorly studied. DESIGN AND METHODS: Data from Rebulla et al. were analyzed in an exploratory analysis using multivariate Cox regression analyses for time-to-first bleed with time-depend- ent covariates reflecting measures of clinical and laboratory variables on the previous day. The relationships of the variables with three bleeding categories were studied: mild bleeding (WHO grades 1 and 2) clinically significant (bleeding grades 2, 3 and 4) and severe (bleeding grades 3 and 4). RESULTS: Bleeding of any severity occurred in 149 (58.4%) of 255 patients. There were 743 days of bleeding over 7335 patient-days of observation. Risk factors for mild bleeding included increased body temperature and decreased platelet count; the risk was decreased with administration of antifungal medication or platelet transfusion on the previous day. Risk factors for clinically significant bleeding included grade 1 bleeding on the previous day, decreased platelet count and elevated body temperature. Decreased platelet count and mild bleeding on the previous day were risk factors for severe bleeding. Higher hemoglobin values were associated with a delay in the time-to-first clinically significant bleed. INTERPRETATION AND CONCLUSIONS: These results support clinical guidelines for increasing the platelet transfusion threshold in the presence of fever and support the use of milder bleeding symptoms as an outcome in clinical trials. The suggestion that hemo- globin concentration maybe predictive of bleeding risk supports the hypothesis that this maybe a valuable intervention in anemic thrombocytopenic patients at high risk of bleeding.
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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 | 0.005 |
| 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 | 0.000 | 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".