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Record W4400367079 · doi:10.1016/j.beha.2024.101556

The role of registries in hematological disorders

2024· review· en· W4400367079 on OpenAlexaff
Helen Baldomero, Daniel Neumann, Nada Hamad, Yoshiko Atsuta, Anna Sureda, Minako Iida, Amado Karduss, Alaa Elhaddad, Nosa Bazuaye, Carmem Bonfim, Rafael de la Cámara, Naeem Chaudhri, Fabio Ciceri, Cinthya Correa, Cristóbal Frutos, Sebastián Galeano, Laurent Garderet, Raffaella Greco, Gregorio Jaimovich, Yoshihisa Kodera, Mickey Koh, Kai‐Yan Liu, Per Ljungman, Donal P. McLornan, Gayathri Nair, Shinichiro Okamoto, Marcelo C. Pasquini, Kristjan Paulson, Annalisa Ruggeri, Adriana Seber, John A. Snowden, Alok Srivastava, Nina Worel, Wael Saber, Damiano Rondelli, Mahmoud Aljurf, Dietger Niederwieser

Bibliographic record

VenueBest Practice & Research Clinical Haematology · 2024
Typereview
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsUniversity of ManitobaInstitute of Infection and Immunity
FundersUniversität Leipzig
KeywordsHematological disordersMedicineHematologic disordersIntensive care medicineImmunology

Abstract

fetched live from OpenAlex

Hematopoietic cell transplantation (HCT) was developed more than 65 years ago to treat malignant blood disorders and irreversible bone marrow failures, with the aim of replacing a diseased hematopoietic system with a healthy one (allogeneic HCT). Decades later, the procedure was adapted to apply maximal chemotherapy or radiotherapy, which would result in bone marrow failure, but could be remedied by an infusion of a patient's own cryopreserved bone marrow (autologous HCT). Both treatments are high-risk and complex, especially during the initial phases. However, concerted efforts, vision, and collaboration between physicians and centers worldwide have resulted in HCT becoming a standard of care for many hematological disorders with progressive improvements in outcomes. Registries and the collaboration of societies worldwide have enabled the delivery of this curative therapy to many patients with fatal hematological diseases. More than 1.5 million HCT were performed between 1957 and 2019, and activity is continuously increasing worldwide.

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)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.002

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 head0.261
GPT teacher head0.581
Teacher spread0.320 · 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 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

Citations4
Published2024
Admission routes1
Has abstractyes

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