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Record W2733429355 · doi:10.1182/blood-2017-03-772368

Increasing use of allogeneic hematopoietic cell transplantation in patients aged 70 years and older in the United States

2017· article· en· W2733429355 on OpenAlexaff
Lori Muffly, Marcelo C. Pasquini, Michael J. Martens, Ruta Brazauskas, Xiaochun Zhu, Kehinde Adekola, Mahmoud Aljurf, Karen K. Ballen, Ashish Bajel, Frédéric Baron, Minoo Battiwalla, Amer Beitinjaneh, Jean‐Yves Cahn, Mathew Carabasi, Yi‐Bin Chen, Saurabh Chhabra, Stefan O. Ciurea, Edward A. Copelan, Anita D’Souza, John Edwards, James M. Foran, César O. Freytes, Henry C. Fung, Robert Peter Gale, Sergio Giralt, Shahrukh K. Hashmi, Gerhard Hildebrandt, Vincent T. Ho, Ann A. Jakubowski, Hillard M. Lazarus, Marlise R. Luskin, Rodrigo Martino, Richard T. Maziarz, Philip L. McCarthy, Taiga Nishihori, Rebecca L. Olin, Richard F. Olsson, Attaphol Pawarode, Edward Peres, Andrew R. Rezvani, David A. Rizzieri, Bipin N. Savani, Harry C. Schouten, Mitchell Sabloff, Matthew D. Seftel, Sachiko Seo, Mohamed L. Sorror, Jeff Szer, Baldeep Wirk, William A. Wood, Andrew Artz

Bibliographic record

VenueBlood · 2017
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsCancerCare ManitobaOttawa HospitalUniversity of Ottawa
FundersNational Cancer InstituteNational Heart, Lung, and Blood Institute
KeywordsMedicineHematopoietic cellTransplantationComorbidityHematopoietic stem cell transplantationHematologic NeoplasmsPediatricsGerontologyStem cellInternal medicineHaematopoiesis

Abstract

fetched live from OpenAlex

Key Points Over the last decade, allogeneic HCT has been increasingly administered in the United States to adults aged 70 and older with hematologic malignancies. Allogeneic transplant outcomes were reasonable; high comorbidity and ablative conditioning regimens were associated with inferior outcomes.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.251
Teacher spread0.231 · 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 designObservational
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

Citations296
Published2017
Admission routes1
Has abstractyes

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