MétaCan
Menu
Back to cohort
Record W4383561141 · doi:10.14740/jh1124

Donor Cell Leukemia Following Allogeneic Hematopoietic Stem Cell Transplantation

2023· article· en· W4383561141 on OpenAlexvenueno aff
Ahmed Khattab, Sunita Patruni, Gina Patrus, Yazan Samhouri, Salman Fazal, John Lister

Bibliographic record

VenueJournal of Hematology · 2023
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsnot available
FundersAllegheny Health Network
KeywordsMedicineStem cellTransplantationLeukemiaHematopoietic stem cell transplantationHaematopoiesisDiseaseImmunologyOncologyPathologyInternal medicineBiology

Abstract

fetched live from OpenAlex

Approximately 25,000 allogeneic transplants are performed annually worldwide; a figure that has steadily increased over the past three decades. The study of transplant recipient survivorship has become a cogent topic and post-transplant donor cell pathology warrants further study. Donor cell leukemia (DCL) is a rare but serious complication of allogeneic stem cell transplantation (SCT) where the recipient develops a form leukemia originating from the donor cells used for transplantation. Detection of abnormalities predicting donor cell pathology might inform donor selection, and the design of survivorship programs for early detection of these abnormalities might allow therapeutic intervention earlier in the disease course. We present four recipients of allogeneic hematopoietic stem cell transplant (HSCT) from our institution who developed donor cell abnormalities allogeneic SCT, highlighting their clinical characteristics and challenges.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.293
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreEmpirical

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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal of HematologySame topicAcute Myeloid Leukemia ResearchFrench-language works237,207