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Assessment of 2 Cervical Screening Methods in Mongolia

2006· article· en· W1971270675 on OpenAlexaff
Laurie Elit, G. Baigal, Jeffrey Tan, A Munkhtaivan

Bibliographic record

VenueJournal of Lower Genital Tract Disease · 2006
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineColposcopyCervical intraepithelial neoplasiaCytologyVisual inspectionBiopsyPredictive valuePopulationGynecologyObstetricsInternal medicineCervical cancerPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of the study was to evaluate the test parameters of visual inspection with acetic acid (VIA) and cervical cytology in 3 Mongolian aimags. METHODS: From February 18, 2002, to December 12, 2004, sexually active women, 30 years or older who had never been screened, underwent cervical cytology and VIA in the aimags' central hospital. Women with abnormal test results and 5% of women with normal results were recommended to have colposcopy with or without biopsy. RESULTS: Two thousand nine women underwent both tests. Visual inspection with acetic acid was abnormal in 254 (12.6%); Pap smear showed atypical squamous cells of undetermined significance or worse in 3%. Using cervical intraepithelial neoplasia 2 or higher disease on biopsy as the end point, the test parameters for VIA are sensitivity of 82.9% (95% CI = 81.3%-84.5%), specificity of 88.6% (95% CI = 87.2%-90.0%), positive predictive value of 12.2% (95% CI = 10.8%-13.6%), and negative predicative value of 99.7% (95% CI = 99.5%-99.9%). The test parameters for Pap smear are sensitivity of 88.6% (95% CI = 87.2%-90.0%), specificity of 98.5% (95% CI = 98.0%-99.0%), positive predictive value of 51.7% (95% CI = 49.5%-53.9%), and negative predicative value of 99.8% (95% CI = 99.6%-100%). CONCLUSION: Visual inspection with acetic acid has an acceptable test parameter for population-based cervical screening in Mongolia.

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)0.001
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 score0.100
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.039
GPT teacher head0.441
Teacher spread0.402 · 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

Citations25
Published2006
Admission routes1
Has abstractyes

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