MétaCan
Menu
Back to cohort
Record W2008639122 · doi:10.1182/blood-2010-01-263764

A population-based study of cardiac morbidity among Hodgkin lymphoma patients with preexisting heart disease

2010· article· en· W2008639122 on OpenAlexafffund
Sten Myrehaug, Melania Pintilie, Lingsong Yun, Michael Crump, Richard Tsang, Ralph M. Meyer, Jonathan Sussman, Eric Yu, David Hodgson

Bibliographic record

VenueBlood · 2010
Typearticle
Languageen
FieldMedicine
TopicChemotherapy-induced cardiotoxicity and mitigation
Canadian institutionsToronto Western HospitalMcMaster UniversityQueen's UniversityJuravinski Cancer CentreInstitute for Clinical Evaluative SciencesUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsMedicineInternal medicinePopulationIncidence (geometry)Heart diseaseHazard ratioLymphomaChemotherapyHeart failureSurgeryConfidence interval

Abstract

fetched live from OpenAlex

The risk of cardiac hospitalization (CH) in Hodgkin lymphoma (HL) patients with preexisting heart disease was evaluated. Patients with HL were identified from a population-based registry (N = 3964). Data were abstracted from records of a randomly selected subcohort (N = 1096). A population-based registry was used to identify CH. Factors associated with CH and the incidence of CH after HL were estimated with competing risk models. Preexisting heart disease was the strongest predictor of posttreatment CH (hazard ratio = 3.98, P < .001) and significantly modified (P = .01) the effect of treatment on the risk of CH. Among patients with preexisting heart disease, treatment with mediastinal radiation therapy plus doxorubicin-based chemotherapy was associated with a 10-year incidence of CH more than 20% higher than treatment with chemotherapy alone. There is a high risk of CH after mediastinal radiation therapy plus doxorubicin-based chemotherapy among patients with preexisting heart disease; this is an important consideration when weighing treatment options, and in the follow-up of these patients.

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 score0.018
Threshold uncertainty score0.410

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 head0.010
GPT teacher head0.239
Teacher spread0.230 · 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

Citations83
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueBloodSame topicChemotherapy-induced cardiotoxicity and mitigationFrench-language works237,207