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Healthcare resource use in the management of advanced epithelial ovarian cancer in Canada.

2025· article· en· W4410814172 on OpenAlexafffundabout
Kimberly Guinan, Laurie Demers-Rozon, Yoann Brassard, Nancy Paul Roc, Hélène Hall, Nathan F. Schachter, Anna V. Tinker, Josée-Lyne Ethier, Shannon Salvador, Stéphane Barakat, Francesc Sorio, Jean Lachaîne

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsSunnybrook Health Science CentreJewish General HospitalABB (Canada)
FundersAbbVie Canada
KeywordsMedicineHealth careOvarian cancerEpithelial ovarian cancerOncologyCancerGynecologyInternal medicineCancer researchEconomic growth

Abstract

fetched live from OpenAlex

e17565 Background: Treatment selection for recurrent advanced epithelial ovarian cancer (EOC) depends on sensitivity to platinum-based chemotherapy (PBC), with patients being categorized as platinum-sensitive or platinum-resistant ovarian cancer (PSOC and PROC, respectively). While most patients initially respond to PBC, resistance often develops resulting in limited treatment options and poor prognosis. Managing advanced EOC requires long-term, intensive treatment and monitoring. However, no Canadian data on healthcare resource utilization (HCRU) costs reflect the current market for managing advanced EOC. Mirvetuximab soravtansine (MIRV), a new therapy intended to treat patients with folate receptor alpha positive PROC, showed a significant benefit over chemotherapy in the MIRASOL trial. The current study aimed to estimate the Canadian HCRU cost for managing advanced EOC, including PSOC and PROC. The secondary objective was to assess the HCRU costs of MIRV versus current PROC therapies, to inform key stakeholders in healthcare decision-making. Methods: A costing analysis was developed via a decision tree to assess the treatment pathway of patients as they remain PSOC or develop PROC. Based on consensus from Canadian clinicians, an average of three lines of therapy (LOT) was assumed for PSOC patients, based on front-line response (front-line PSOC), including patients eventually developing platinum resistance. For PROC patients based on front-line response (front-line PROC), two LOTs were assumed. PSOC treatments included PBC and maintenance while PROC included bevacizumab+chemotherapy and single-agent chemotherapies. Pretreatment, administration, monitoring, adverse events grade ≥3 at a frequency of >5%, inpatient and emergency visits, and end-of-life costs were included. Drug acquisition costs were excluded to assess disease management costs. Model inputs were derived from product labels and validated by Canadian clinicians to reflect current practice. A societal perspective was also assessed. Results: Patients with PSOC who remained platinum-sensitive incurred $73,350 HCRU costs compared to $71,140 for front-line PROC patients. For patients who develop platinum resistance in later LOT, HCRU costs ranged between $68,107 and $75,786. The cost variation was mainly driven by increased monitoring for PSOC, increased disease-related complications for PROC, as well as the average number of LOT patients received due to differences in prognosis. Among PROC therapies, MIRV was associated with a minimal increase in HCRU costs compared to single-agent chemotherapies (Δ: +$987 to +$1,513) but reduced HCRU costs compared to the most commonly used treatment, bevacizumab + chemotherapy (Δ: -$9 to -$2,537). Conclusions: Advanced EOC is associated with a significant HCRU burden in Canada. Based on this model, introducing MIRV may reduce or, at minimum, prevent further increases in this burden.

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)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score0.848

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.007
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.088
GPT teacher head0.460
Teacher spread0.372 · 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".

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Citations0
Published2025
Admission routes3
Has abstractyes

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