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Record W2970040822 · doi:10.1016/j.bbmt.2019.08.012

Comparison of High Doses of Total Body Irradiation in Myeloablative Conditioning before Hematopoietic Cell Transplantation

2019· article· en· W2970040822 on OpenAlexaff
Mitchell Sabloff, Saurabh Chhabra, Tao Wang, Caitrin Fretham, Natasha Kekre, Allistair Abraham, Kehinde Adekola, Jeffery J. Auletta, Christopher A. Barker, Amer Beitinjaneh, Christopher Bredeson, Jean‐Yves Cahn, Miguel Ángel Díaz, César O. Freytes, Robert Peter Gale, Siddhartha Ganguly, Usama Gergis, Eva C. Guinan, Betty K. Hamilton, Shahrukh K. Hashmi, Peiman Hematti, Gerhard Hildebrandt, Leona Holmberg, Sanghee Hong, Hillard M. Lazarus, Rodrigo Martino, Lori Muffly, Taiga Nishihori, Miguel‐Angel Perales, Jean A. Yared, Shin Mineishi, Edward A. Stadtmauer, Marcelo C. Pasquini, Alison W. Loren

Bibliographic record

VenueBiology of Blood and Marrow Transplantation · 2019
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsOttawa HospitalUniversity of Ottawa
FundersNational Institute of Allergy and Infectious DiseasesOffice of Naval ResearchPharmacyclicsKite PharmaHealth Resources and Services AdministrationNational Institutes of HealthCytoSen TherapeuticsSwedish Orphan BiovitrumJanssen Scientific AffairsMundipharma EDOAstellas PharmaAdaptive BiotechnologiesPfizerIncytebluebird bioMedical College of WisconsinChimerixMedacJazz PharmaceuticalsTakeda OncologyUniversity of MinnesotaKaryopharm TherapeuticsSanofi GenzymePatient-Centered Outcomes Research InstituteHistoGeneticsU.S. NavyCelgeneU.S. Department of DefenseSanofiSpectrum PharmaceuticalsAmgenGovernment of South AustraliaNational Heart, Lung, and Blood InstituteNovartis Pharmaceuticals CorporationBristol-Myers SquibbAstellas Pharma USAtara BiotherapeuticsNational Cancer InstituteGilead Sciences
KeywordsMedicineTotal body irradiationConditioningHematopoietic cellTransplantationHematopoietic stem cell transplantationHaematopoiesisInternal medicineStem cellCyclophosphamideChemotherapy

Abstract

fetched live from OpenAlex

Malignancy relapse is the most common cause of treatment failure among recipients of hematopoietic cell transplantation (HCT). Conditioning dose intensity can reduce disease relapse but is offset by toxicities. Improvements in radiotherapy techniques and supportive care may translate to better outcomes with higher irradiation doses in the modern era. This study compares outcomes of recipients of increasing doses of high-dose total body irradiation (TBI) divided into intermediate high dose (IH; 13-13.75 Gy) and high dose (HD; 14 Gy) with standard dose (SD; 12 Gy) with cyclophosphamide. A total of 2721 patients ages 18 to 60 years with hematologic malignancies receiving HCT from 2001 to 2013 were included. Cumulative incidences of nonrelapse mortality (NRM) at 5 years were 28% (95% confidence interval [CI], 25% to 30%), 32% (95% CI, 29% to 36%), and 34% (95% CI, 28% to 39%) for SD, IH, and HD, respectively (P = .02). Patients receiving IH-TBI had a 25% higher risk of NRM compared with those receiving SD-TBI (12 Gy) (P = .007). Corresponding cumulative incidences of relapse were 36% (95% CI, 34% to 38%), 32% (95% CI, 29% to 36%), and 26% (95% CI, 21% to 31%; P = .001). Hazard ratios for mortality compared with SD were 1.06 (95% CI, .94 to 1.19; P = .36) for IH and .89 (95% CI, .76 to 1.05; P = .17) for HD. The study demonstrates that despite improvements in supportive care, myeloablative conditioning using higher doses of TBI (with cyclophosphamide) leads to worse NRM and offers no survival benefit over SD, despite reducing disease relapse.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.267
Teacher spread0.258 · 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 designBench or experimental
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

Citations33
Published2019
Admission routes1
Has abstractyes

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