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Record W2792053139 · doi:10.1377/hlthaff.2017.1045

Quality Of Breast Cancer Care In The US Territories: Insights From Medicare

2018· article· en· W2792053139 on OpenAlexaff
Tracy M. Layne, Jenerius A. Aminawung, Pamela R. Soulos, Marcella Nuñez-Smith, Maxine Nunez, Beth A. Jones, Karen Wang, Cary P. Gross

Bibliographic record

VenueHealth Affairs · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBreast Cancer Treatment Studies
Canadian institutionsSmiths Detection (Canada)
FundersNational Institute on Minority Health and Health DisparitiesNational Center for Advancing Translational SciencesNational Cancer Institute
KeywordsMedicineOddsBreast cancerOdds ratioHealth careCancerReceiptBiopsyPopulationMastectomyFamily medicineLogistic regressionInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

The quality of breast cancer care among Medicare beneficiaries in the US territories-where federal spending for health care is lower than in the continental US-is unknown. We compared female Medicare beneficiaries who were residents of the US territories and had surgical treatment for breast cancer in 2008-14 to those in the continental US in terms of receipt of recommended breast cancer care (diagnostic needle biopsy and adjuvant radiation therapy [RT] following breast-conserving surgery) and the timeliness (time from needle biopsy to surgery and from surgery to adjuvant RT) of that care. Residents of the US territories were less likely to receive recommended care (24 percent lower odds of receiving diagnostic needle biopsy and 34 percent lower odds of receiving adjuvant RT) and to receive timely care (45 percent lower odds of receiving surgery and 82 percent lower odds of receiving adjuvant RT, both within three months). Further research is needed to identify barriers to the provision of adequate and timely breast cancer care in this unique population.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.079
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.326
Teacher spread0.312 · 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.

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".

Quick stats

Citations8
Published2018
Admission routes1
Has abstractyes

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