Can we transfuse wisely in patients undergoing chemotherapy for acute leukemia or autologous stem cell transplantation?
Bibliographic record
Abstract
BACKGROUND: Transfusion of 2 units of red blood cells (RBCs) for Hb ≤80 g/L is the prevailing liberal practice for patients undergoing intensive treatment for acute leukemia or hematopoietic transplant across North America. There is little evidence regarding optimal transfusion targets in these highly transfusion-dependent patient populations. STUDY DESIGN AND METHODS: This was a retrospective pre-post cohort study of consecutive patients admitted to Kingston Health Sciences Center between April through December 2016 (pre) and April through December 2017 (post) for acute leukemia induction chemotherapy or high dose chemotherapy (HDCT) for autologous stem cell transplantation (ASCT). The pre-cohort was transfused using a liberal threshold (2 units of RBCs for Hb ≤80 g/L) and the post-cohort using a more restrictive threshold (1 unit RBCs for Hb ≤70 g/L), implemented with a computerized physician order entry form. Primary outcome was number of RBC units transfused per inpatient day. Secondary outcomes included inpatient mortality and select morbidity measures. RESULTS: 124 patients underwent 134 treatment courses: 62 courses of induction chemotherapy (pre = 26, post = 36) and 72 courses of HDCT for ASCT (pre = 39, post = 33). There was a significant decrease in median RBC utilization per admission in both patient populations: 10.5 versus 6.7 in the leukemia group (p = 0.01) and 2.0 versus 1.0 in the ASCT group (p = 0.04). This reduction was seen without a difference in inpatient mortality, length of stay, falls, serious bleeds, requirement for ICU, or time to engraftment post ASCT. CONCLUSIONS: A restrictive transfusion strategy in patients receiving intensive chemotherapy for acute leukemia or ASCT decreased inpatient RBC usage without increasing adverse inpatient events.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".