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Record W2051054241 · doi:10.1089/jwh.2012.4203

Evaluating the Effectiveness of Cervical Cancer Screening Invitation Letters

2013· article· en· W2051054241 on OpenAlexafffund
Kathleen Decker, Donna Turner, Alain Demers, Patricia J. Martens, Pascal Lambert, Daniel Chateau

Bibliographic record

VenueJournal of Women s Health · 2013
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsUniversity of ManitobaCancerCare ManitobaManitoba Health
FundersCanadian Institutes of Health Research
KeywordsMedicineConfidence intervalRandomized controlled trialOdds ratioTest (biology)Cervical cancerPap testCervical cancer screeningCervical screeningGynecologyFamily medicineDemographyCancerInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The objective of this study was to evaluate the effectiveness of an invitation letter on cervical screening participation among unscreened women 30 to 69 years of age. METHODS: A cluster randomized trial design was used in which unscreened women (n=31,452) were randomized by the forward sortation area (FSA) of their postal code to an intervention group that was sent an invitation letter (n=17,068) or a group that was not sent an invitation letter (n=14,384). RESULTS: Six months after the letters were mailed, 1,010 women in the intervention group (5.92%) and 441 women in the control group (3.06%) had a Pap test. After adjusting for variables that have previously shown to influence screening participation, women who were sent an invitation letter were significantly more likely to have had a Pap test in the next 6 months compared with women who were not sent an invitation letter (odds ratio [OR]=2.60, 95% confidence interval [CI] 2.09-3.35, p<0.001). Overall, the effectiveness of the invitation letter improved with increasing age (p=0.02). CONCLUSIONS: Sending invitation letters increased cervical screening participation but because the overall effect was small, additional strategies that remove barriers to screening for unscreened women are also necessary.

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)0.006
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.878
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
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)0.0010.000

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 head0.096
GPT teacher head0.471
Teacher spread0.375 · 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

Citations31
Published2013
Admission routes2
Has abstractyes

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