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Record W4406146832 · doi:10.51731/cjht.25.1055

Capivasertib (Truqap)

2025· article· en· W4406146832 on OpenAlexaboutno aff
CDA-AMC

Bibliographic record

VenueCanadian Journal of Health Technologies · 2025
Typearticle
Languageen
FieldMedicine
TopicAdvanced Breast Cancer Therapies
Canadian institutionsnot available
Fundersnot available
KeywordsFulvestrantReimbursementMedicineOncologyMetastatic breast cancerRegimenInternal medicineAnastrozoleCancerBreast cancerTamoxifenPolitical science

Abstract

fetched live from OpenAlex

Reimbursement reviews are comprehensive assessments of the clinical effectiveness and cost-effectiveness, as well as patient and clinician perspectives, of a drug or drug class. The assessments inform nonbinding recommendations that help guide the reimbursement decisions of Canada’s federal, provincial, and territorial governments, with the exception of Quebec. This review assesses capivasertib (Truqap) 400 mg (2 tablets of 200 mg each) taken orally twice daily for 4 days followed by 3 days off treatment in combination with fulvestrant 500 mg, administered intramuscularly on days 1, 15, and 29, then monthly thereafter. Indication: Capivasertib is indicated in combination with fulvestrant for the treatment of adult females with hormone receptor–positive, human epidermal growth factor receptor 2–negative, locally advanced or metastatic breast cancer with 1 or more PIK3CA, AKT1, or PTEN alterations following progression on at least 1 endocrine-based regimen in the metastatic setting or recurrence on or within 12 months of completing, adjuvant therapy.

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)0.001
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: none
Teacher disagreement score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0150.003

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 head0.016
GPT teacher head0.317
Teacher spread0.301 · 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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Same venueCanadian Journal of Health TechnologiesSame topicAdvanced Breast Cancer TherapiesFrench-language works237,207