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Record W2573622497 · doi:10.1002/cncr.30546

Impact of pre‐transplant depression on outcomes of allogeneic and autologous hematopoietic stem cell transplantation

2017· article· en· W2573622497 on OpenAlexaff
Areej El‐Jawahri, Yi‐Bin Chen, Ruta Brazauskas, Naya He, Stephanie J. Lee, Jennifer M. Knight, Navneet S. Majhail, David Buchbinder, Raquel M. Schears, Baldeep Wirk, William A. Wood, Ibrahim Ahmed, Mahmoud Aljurf, Jeff Szer, Sara Beattie, Minoo Battiwalla, Christopher E. Dandoy, Miguel Ángel Díaz, Anita D’Souza, César O. Freytes, James Gajewski, Usama Gergis, Shahrukh K. Hashmi, Ann A. Jakubowski, Rammurti T. Kamble, Tamila L. Kindwall‐Keller, Hilard M. Lazarus, Adriana K. Malone, David I. Marks, Kenneth R. Meehan, Bipin N. Savani, Richard F. Olsson, David A. Rizzieri, Amir Steinberg, Dawn Speckhart, David Szwajcer, Hélène Schoemans, Sachiko Seo, Celalettin Üstün, Yoshiko Atsuta, Jignesh Dalal, Carmem Sales‐Bonfim, Nandita Khera, Theresa Hahn, Wael Saber

Bibliographic record

VenueCancer · 2017
Typearticle
Languageen
FieldMedicine
TopicCancer survivorship and care
Canadian institutionsUniversity of ManitobaUniversity of Ottawa
FundersNational Institute of Allergy and Infectious DiseasesOffice of Naval ResearchGenentechHealth Resources and Services AdministrationOnyx PharmaceuticalsUniversity of MinnesotaMedical College of WisconsinTherakosNational Heart, Lung, and Blood InstituteNovartis Pharmaceuticals CorporationSt. Baldrick's FoundationOtsuka America PharmaceuticalFred Hutchinson Cancer Research CenterAlexion PharmaceuticalsNational Cancer InstituteGenzymeSeattle GeneticsU.S. Department of Defense
KeywordsMedicineDepression (economics)Hazard ratioTransplantationInternal medicineIncidence (geometry)Hematopoietic stem cell transplantationProportional hazards modelConfidence intervalSurgeryOncology

Abstract

fetched live from OpenAlex

BACKGROUND: To evaluate the impact of depression before autologous and allogeneic hematopoietic cell transplantation (HCT) on clinical outcomes post-transplantation. METHODS: We analyzed data from the Center for International Blood and Marrow Transplant Research to compare outcomes after autologous (n = 3786) or allogeneic (n = 7433) HCT for adult patients with hematologic malignancies with an existing diagnosis of pre-HCT depression requiring treatment versus those without pre-HCT depression. Using Cox regression models, we compared overall survival (OS) between patients with or without depression. We compared the number of days alive and out of the hospital in the first 100 days post-HCT using Poisson models. We also compared the incidence of grade 2-4 acute and chronic graft-versus-host disease (GVHD) in allogeneic HCT. RESULTS: The study included 1116 (15%) patients with pre-transplant depression and 6317 (85%) without depression who underwent allogeneic HCT between 2008 and 2012. Pre-transplant depression was associated with lower OS (hazard ratio [HR], 1.13; 95% confidence interval [CI], 1.04-1.23; P = 0.004) and a higher incidence of grade 2-4 acute GVHD (HR, 1.25; 95% CI, 1.14-1.37; P < 0.0001), but similar incidence of chronic GVHD. Pre-transplant depression was associated with fewer days-alive-and-out-of-the hospital (means ratio [MR] = 0.97; 95% CI, 0.95-0.99; P = 0.004). There were 512 (13.5%) patients with Pre-transplant depression and 3274 (86.5%) without depression who underwent autologous HCT. Pre-transplant depression in autologous HCT was not associated with OS (HR, 1.15; 95% CI, 0.98-1.34; P = 0.096) but was associated with fewer days alive and out of the hospital (MR, 0.98; 95% CI, 0.97-0.99; P = 0.002). CONCLUSION: Pre-transplant depression was associated with lower OS and higher risk of acute GVHD among allogeneic HCT recipients and fewer days alive and out of the hospital during the first 100 days after autologous and allogeneic HCT. Patients with pre-transplant depression represent a population that is at risk for post-transplant complications. Cancer 2017;123:1828-1838. © 2017 American Cancer Society.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.021
GPT teacher head0.323
Teacher spread0.302 · 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 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

Citations100
Published2017
Admission routes1
Has abstractyes

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