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Record W1970354605 · doi:10.1097/lgt.0b013e31823da811

An Audit of the Cervical Cancer Screening Histories of 246 Women With Carcinoma

2012· article· en· W1970354605 on OpenAlexaffabout
Máire A. Duggan, Jill Nation

Bibliographic record

VenueJournal of Lower Genital Tract Disease · 2012
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsFoothills Medical CentreUniversity of Calgary
FundersMicrosoft
KeywordsMedicineCervical carcinomaAuditCervical cancerOncologyCervical cancer screeningInternal medicineGynecologyObstetricsFamily medicineCancer

Abstract

fetched live from OpenAlex

OBJECTIVE: Women with cervical carcinoma and residing in the Calgary Health Region between 1996 and 2001 were audited to characterize factors in the opportunistic cervical cancer screening pathway contributing to screening failures. MATERIALS AND METHODS: The cohort consisted of 246 women. Information on their Pap tests and colposcopic/gynecologic examinations was obtained from the files of Calgary Laboratory Services and their colposcopic/cancer center treatment charts. Screening failure factors were defined, and frequencies were calculated. RESULTS: Screening failure factors were as follows: (1) 41 (16.7%) were not screened, that is, no Pap test screening; (2) 29 (11.8%) were underscreened, that is, no Pap test within 12 months of diagnosis; (3) 28 (13.7%) were undersampled, that is, the Pap test result was negative; (4) 34 (13.8%) had no referral for a colposcopy/gynecology examination, and/or it was delayed for more than 3 months; (5) 18 (13.2%) had delayed referral for examination of an atypical glandular cell-high-grade squamous intraepithelial lesion and higher Pap test for more than 3 months; and (6) 73 (55.3%) were underdiagnosed, that is, the diagnosis in colposcopy examination was less than malignant. Underreported Pap tests and delayed Pap test reporting could not be fully investigated, but limited evidence suggested that underreporting contributed to some failures. CONCLUSIONS: Factors other than recruitment to cytological screening need targeted improvement if the region's cervical cancer prevention program is to be more effective.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.999

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)0.0020.000

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 head0.027
GPT teacher head0.314
Teacher spread0.287 · 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 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

Citations17
Published2012
Admission routes2
Has abstractyes

Explore more

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