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Record W2024162173 · doi:10.1158/1055-9965.disp-10-a13

Abstract A13: How do breast imaging centers communicate results: A national survey

2010· article· en· W2024162173 on OpenAlexaboutno aff
Erin N. Marcus, Tulay Koru‐Sengul

Bibliographic record

VenueCancer Epidemiology Biomarkers & Prevention · 2010
Typearticle
Languageen
FieldMedicine
TopicGlobal Cancer Incidence and Screening
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineQuarter (Canadian coin)Family medicineMedicaidMammographyDescriptive statisticsBreast imagingBreast cancerHealth careCancer

Abstract

fetched live from OpenAlex

Abstract Background: Effective communication of mammogram results is important as a step toward reducing patient anxiety and diagnostic delay. Research suggests, however, that many women do not correctly understand their results. The purpose of this study is to describe current communication practices among a nationally representative sample of mammography centers. Methods: A 35-question anonymous online survey was sent to members of the National Consortium of Breast Centers (NCBC), an association of more than 2000 physicians, nurses, administrators, radiology technicians, and others involved in breast care. Those involved in mammography were invited to participate. The survey was also distributed at the NCBC's annual meeting. It asked about centers’ verbal, written, and telephone communication with patients, whether they employ patient navigators and staff with fluency in languages other than English, and whether and how they contact patients who do not follow up. Descriptive statistics were calculated for all responses. Additional analysis to assess the association of demographic variables with responses is ongoing. Results: 221 centers completed the questionnaire. 34% were affiliated with a private radiology practice, 26% were academically affiliated, and 16% were free-standing centers. Nearly half of the centers indicated that more than a quarter of their patients lacked insurance and/or relied on government programs such as Medicaid. Ten percent of the centers indicated that more than a quarter of their patients lacked English proficiency. Ten percent of the respondents indicated that they rarely inform patients of their results at the time of their mammogram; 17% indicated that they inform patients all or most of the time; 35% indicated that the result letters they send are English only; and 12% indicated that they do not have multilingual staff or translators available to answer questions. 22% of the centers indicated that they use patient navigators. 16% of respondents indicated that they do not routinely telephone any patients about results. Chi-square analysis to detect associations between variables is ongoing. Conclusions and Implications: Our sample included a diverse cross-section of mammogram centers, many of which serve low-income patients. More than one in three centers indicated that they do not send result letters in languages other than English, which is concerning given the increasing number of patients with limited English proficiency in the U.S. and past research indicating many women do not understand their result. One in five centers use patient navigators, however, which may help ensure appropriate follow up among patients who lack easy access to healthcare. Informing patients of their results at the time of their test may also be a step more centers could take to enhance patient understanding and reduce anxiety. Citation Information: Cancer Epidemiol Biomarkers Prev 2010;19(10 Suppl):A13.

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)0.005
metaresearch head score (Gemma)0.015
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 score0.009
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.172
GPT teacher head0.434
Teacher spread0.262 · 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".

Quick stats

Citations0
Published2010
Admission routes1
Has abstractyes

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