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Record W4415403106 · doi:10.51731/cjht.2025.1258

Acalabrutinib (Calquence)

2025· article· W4415403106 on OpenAlexaboutno aff
CDA-AMC

Bibliographic record

VenueCanadian Journal of Health Technologies · 2025
Typearticle
Language
FieldMedicine
TopicChronic Lymphocytic Leukemia Research
Canadian institutionsnot available
Fundersnot available
KeywordsBendamustineMantle cell lymphomaDebulkingRituximabLymphomaPrimary central nervous system lymphoma

Abstract

fetched live from OpenAlex

Canada’s Drug Agency (CDA-AMC) recommends that Calquence should be reimbursed by public drug plans for the treatment of adult patients with previously untreated mantle cell lymphoma (MCL) who are ineligible for autologous stem cell transplant (ASCT) in combination with bendamustine and rituximab (BR), if certain conditions are met. Calquence plus BR should only be covered to treat patients aged 65 years or older with previously untreated MCL, with a good performance status. Calquence plus BR should not be covered in patients who have a history of central nervous system lymphoma or leptomeningeal disease, or if the goal of therapy is tumour debulking before stem cell transplant. Calquence plus BR should only be reimbursed if prescribed by clinicians with expertise in managing MCL and monitoring therapy, and if the cost of Calquence is reduced.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.045
Threshold uncertainty score0.152

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0450.013

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 head0.028
GPT teacher head0.355
Teacher spread0.327 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

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