Shifting ground and gaps in transfusion support of patients with hematological malignancies
Bibliographic record
Abstract
The transfusion support of hematological malignancies considers 2 dimensions: the quantity of what we order (in terms of triggers, doses, targets, and intervals), and the special qualities thereof (with respect to depths of matching and appropriate product modifications). Meanwhile, transfusion-related enhancements in the quantity and quality of life may not be dose dependent but rather tempered by unintended patient harms and system strains from overexposure. Evidence and guidelines concur in endorsing clinically noninferior conservative red blood cell (RBC) transfusion care strategies (eg, triggering at hemoglobin <7-8 g/dL and in single-unit doses for stable, nonbleeding inpatients). However, the unique subpopulation of patients with hematological malignancies who are increasingly managed on an outpatient basis, and striving at least as much for quality of life as quantity of life, is left on the edges of these recommendations, with more questions than answers. If a sufficiently specific future wave of evidence can satisfy the concerns (and contest the assumptions) of the remaining proponents of liberalism, and if conservatism is broadly adopted, savings may be potentially immense. These savings can then be reinvested to address other gaps and inconsistencies in RBC transfusion care, such as the best achievable degrees of prophylactic antigen matching that can minimize alloimmunization-related service delays and reactions.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".