MétaCan
Menu
Back to cohort

Shifting ground and gaps in transfusion support of patients with hematological malignancies

2018· review· en· W2903122112 on OpenAlexaff
Christine Cserti‐Gazdewich

Bibliographic record

VenueHematology · 2018
Typereview
Languageen
FieldMedicine
TopicHemoglobinopathies and Related Disorders
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineCONTESTIntensive care medicineConservatismQuality (philosophy)Blood transfusionMatching (statistics)Internal medicinePolitical science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.904
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.281
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations5
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueHematologySame topicHemoglobinopathies and Related DisordersFrench-language works237,207