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Record W3136954491 · doi:10.3390/curroncol28020119

Evaluating the Indirect Costs of Care Associated with Salvage Chemotherapy for Relapsed and Refractory Aggressive-Histology Lymphoma: A Subset Analysis of the Canadian Cancer Trials Group (CCTG) LY.12 Clinical Trial

2021· article· en· W3136954491 on OpenAlexaffvenueabout
Anca Prica, Annette E. Hay, Michael Crump, Nicole Mittmann, Lois E. Shepherd, Ralph M. Meyer, Kevin I. Imrie, Nancy Risebrough, Marina Djurfeldt, Bingshu E. Chen, Matthew C. Cheung

Bibliographic record

VenueCurrent Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineClinical trialInternal medicineSalvage therapyChemotherapyLymphomaOncologyRefractory (planetary science)WorkloadRandomized controlled trialIndirect costs

Abstract

fetched live from OpenAlex

We conducted an analysis of indirect costs alongside the LY.12 randomized trial in patients with relapsed or refractory (R/R) aggressive non-Hodgkin lymphoma (NHL). Lost productivity data for Canadian patients and caregivers in the trial were collected at baseline and with each chemotherapy cycle pre-transplant, using an adapted Lost Productivity questionnaire. Mean per patient indirect costs were CAD 2999 for patients in the GDP arm and CAD 3400 in the DHAP arm. A substantial majority was not working or had to reduce their workload during this treatment time. Salvage chemotherapy for R/R aggressive NHL is associated with significant indirect costs to patients and their caregivers.

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)0.010
metaresearch head score (Gemma)0.016
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.793
Threshold uncertainty score0.412

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.006
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.270
GPT teacher head0.510
Teacher spread0.240 · 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

Citations11
Published2021
Admission routes3
Has abstractyes

Explore more

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