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Abstract P4-18-01: Waiting for Treatment: Addressing Inequities in access to essential medications for metastatic breast cancer

2017· article· en· W2594485103 on OpenAlexaffabout
Craig Faucette

Bibliographic record

VenueCancer Research · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsCanadian Breast Cancer Network
Fundersnot available
KeywordsFormularyMedicineMetastatic breast cancerReimbursementBreast cancerCancerExpanded accessCancer drugsDiseaseFamily medicineHealth careOncologyInternal medicineEconomic growth

Abstract

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Abstract Introduction: Of the women in Canada diagnosed with breast cancer each year, 5% will have an initial diagnosis of metastatic breast cancer and many more will recurr with metastatic disease. Despite global advancements in metastatic breast cancer, access to new drugs in Canada remains inequitable. Objective: To examine the present situation in Canada for drug approval and decision-making by provinces and territories around new metastatic drugs and to assess processes in the system causing the greatest lags, the range in wait times for new treatments by province and the jurisdictions most affected by wait time delays. Methodology: - A bilingual survey in spring 2015, focusing on access to metastatic treatments across Canada. 98 women responded to the survey from all Canadian jurisdictions except Yukon. -A case study of 4 metastatic therapies undergoing multi-step drug review. Results: Systemic Delays: Patients endure 1-2 years for drugs to be approved for sale in Canada and further delays in access through the pCODR, pCPA and provincial drug assessment processes. The longest delays occur at the provincial review stage. Inequitable Access to Treatments across Provinces: Lack of firm deadlines for the provinces to list a drug on their formulary once a drug has been approved pricing negotiated. Often a 2-year time lag or longer between provinces in making reimbursement decisions, resulting in inequitable access across the country. Line Sequencing Access Delays: Further limitations on access created by the restrictions that provinces place on when drugs can be used in the course of treatment. Formularies often cover older drugs that are less costly, and as such it is a challenge to secure coverage of newer therapies. Conclusion: Wait time delays create a significant barrier to patients receiving optimal treatment and care. Metastatic patients are forced to endure wait time delays of 2-4 years before accessing new treatments. These delays occur at nearly every stage of the approval process, with the greatest waits occurring at the provincial level. Lags in treatment access across the country also have an impact on standards of care. Patients in provinces with timely approvals, have access to more treatment options than those in provinces where patients must wait for new treatments to be added to the formulary. Lengthy wait times for new treatments complicate a patient's prognosis. Slowing the progression of their disease and maintaining a better quality of life is of critical concern and metastatic patients with urgent treatment needs cannot wait for new treatments to become publicly accessible. For these patients, expedited access to a diversity of treatment options can make all the difference in ensuring optimal health outcomes and improved quality of life. Citation Format: Faucette C. Waiting for Treatment: Addressing Inequities in access to essential medications for metastatic breast cancer [abstract]. In: Proceedings of the 2016 San Antonio Breast Cancer Symposium; 2016 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2017;77(4 Suppl):Abstract nr P4-18-01.

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)0.002
metaresearch head score (Gemma)0.009
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.798
Threshold uncertainty score0.406

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.001

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 head0.408
GPT teacher head0.493
Teacher spread0.085 · 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
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
Published2017
Admission routes2
Has abstractyes

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