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Record W4417329605 · doi:10.51731/cjht.2025.1306

Inavolisib (Itovebi)

2025· article· W4417329605 on OpenAlexaboutno aff
CDA-AMC

Bibliographic record

VenueCanadian Journal of Health Technologies · 2025
Typearticle
Language
FieldMedicine
TopicAdvanced Breast Cancer Therapies
Canadian institutionsnot available
Fundersnot available
KeywordsBreast cancerMetastatic breast cancerRegimenReimbursementCancerFulvestrantPalbociclib

Abstract

fetched live from OpenAlex

Canada’s Drug Agency recommends that Itovebi in combination with palbociclib (PAL) and fulvestrant (FUL) be reimbursed by public drug plans for the treatment of adult patients with endocrine-resistant, PIK3CA-mutated, hormone receptor–positive, HER2-negative, locally advanced or metastatic breast cancer, following recurrence on or after completing adjuvant endocrine treatment, only if certain conditions are met. Itovebi in combination with PAL and FUL should only be covered to treat adults with hormone receptor–positive, HER2-negative breast cancer that has spread to nearby tissue or lymph nodes (locally advanced), or to other parts of the body (metastatic); has come back after hormone (endocrine) therapy; and has an abnormal PIK3CA Patients should also have good performance status. Itovebi in combination with PAL and FUL should not be covered if the patient has been previously treated for hormone receptor–positive, HER2-negative metastatic breast cancer with mutations in the PIK3CA gene, or if they have uncontrolled diabetes. Itovebi in combination with PAL and FUL should be prescribed by, then managed under the care of, health care professionals with expertise in managing advanced or metastatic breast cancer. Reimbursement of Itovebi should be discontinued if the cancer becomes worse or there are unacceptable side effects. Price reductions exceeding 90% in the cost of Itovebi as part of the combination regimen with CDK4/6 inhibitor and FUL would be required to achieve an incremental cost-effectiveness ratio below $50,000 per quality-adjusted life-year gained, relative to therapies currently in use.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.018
GPT teacher head0.326
Teacher spread0.308 · 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.

Study designOther design
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

Citations0
Published2025
Admission routes1
Has abstractyes

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