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Record W3183762145 · doi:10.32768/abc.202183243-246

What Do We Mean When We Ask for More Metastatic Breast Cancer Research?

2021· article· en· W3183762145 on OpenAlexafffundabout
Heather Douglas, Catherine Hays, Kimberly Badovinac

Bibliographic record

VenueArchives of Breast Cancer · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Financial Impacts of Cancer
Canadian institutionsCanadian Partnership Against Cancer
FundersCanadian Institutes of Health Research
KeywordsMetastatic breast cancerBreast cancerGeneral partnershipMedicineCancerBaseline (sea)OncologyInvestment (military)Family medicineInternal medicineBusinessPolitical science

Abstract

fetched live from OpenAlex

Background: Almost all deaths from breast cancer are due to metastasis. People living with metastatic breast cancer (MBC) and their loved ones have been concerned about the lack of research progress. The purposes of this paper were to analyze breast cancer research spending in Canada, and to evaluate whether MBC research was aligned with patient priorities. The results from the MBC Priority Setting Partnership (MBC PSP) were used as an approximation of patient priorities. Methods: The data source was the Canadian Cancer Research Survey. MBC projects were identified and mapped to the patient priorities.Results: This analysis found that 18% of breast cancer research investment was directed to MBC, with a large proportion of this research investment focused on the biology of metastasis. Four of the top 10 MBC PSP priorities had not been addressed: optimal sequence of therapy, role of continuous versus intermittent treatment, benefits of early palliative care, and best methods for patient education. Conclusion: These figures provide a baseline from which any increases in MBC research and improved alignment to patient priorities can be measured. A cooperative effort by funders, researchers, patients, caregivers, and health care providers is needed to address research gaps.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.735
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.056
GPT teacher head0.310
Teacher spread0.255 · 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
Published2021
Admission routes3
Has abstractyes

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