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Record W2114912392 · doi:10.1186/1897-4287-11-17

Optimizing recruitment to a prostate cancer surveillance program among male BRCA1 mutation carriers: invitation by mail or by telephone

2013· article· en· W2114912392 on OpenAlexaff
A. Galor, Cezary Cybulski, Jan Lubiński, Steven A. Narod, Jacek Gronwald

Bibliographic record

VenueHereditary Cancer in Clinical Practice · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBRCA gene mutations in cancer
Canadian institutionsCoalition for Research in Women's HealthUniversity of Toronto
Fundersnot available
KeywordsMedicinePhoneFamily medicinePublic healthTelephone callTelecommunicationsNursing

Abstract

fetched live from OpenAlex

The effectiveness of a genetics-based public health screening programs depend on the successful recruitment of subjects who qualify for intensified screening by virtue of a positive genetic test. Herein we compare the effectiveness of a mailed invitation and follow-up phone call for non-responding subjects and an initial invitation by telephone addressed to male BRCA1 mutation carriers for prostate screening.The final participation rate was 75% (42 of 56) for men who were initially contacted by mail (and follow-up phone call) and 81% (30 of 37) for men who were initially contacted by telephone. Among the men who were initially contacted by mail, it was necessary to telephone 54% of these patients (30 of 56).After a calculation of the cost-effectiveness related to these results, we conclude that if the costs of the phone call were to exceed the costs of the letter by 2.5 times or more, then savings would be arranged by initiating contact with a mailed invitation.

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)0.013
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.049
GPT teacher head0.409
Teacher spread0.360 · 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 designNon-randomized trial
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

Citations2
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueHereditary Cancer in Clinical PracticeSame topicBRCA gene mutations in cancerFrench-language works237,207