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Record W4409737620 · doi:10.4103/ijpvm.ijpvm_283_23

The Fundamental Place of Pap Test in Iran, Does Primary HPV-Genotyping Seem Cost-Effective in Replace? A Cohort Study

2025· article· en· W4409737620 on OpenAlexaff
Azam Zafarbakhsh, Fariba Behnamfar, Matin Shariati, Atefeh Vaezi, Leila Mousavi Seresht

Bibliographic record

VenueInternational Journal of Preventive Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineGenotypingCytologyCervical cancerHPV infectionCohortHuman papillomavirusGynecologyCervical screeningObstetricsOncologyCancerInternal medicineGenotypePathologyGene

Abstract

fetched live from OpenAlex

Background: Human papillomavirus (HPV) is a known risk factor for cervical cancer, and currently, primary HPV typing is recommended for screening instead of cervical cytology. However, there are limited studies on the prevalence of HPV in Iran. Methods: This cross-sectional study evaluated the liquid-based cervical smears of 700 women with no history of HPV vaccination and cervical dysplastic disease from 2017 to 2020 in Isfahan, Iran. Here, we compare the prevalence of HPV genotypes using COBAS with Pap smear cytology results in evaluating the most appropriate cervical cancer screening test. Results: The prevalence of HPV infection was 23.3%, including 8.7% with HPV 16/18 and 14.6% with other HR (high-risk) HPVs. In cytology reports, 8 out of 16 individuals with high-risk lesions were negative for any type of HPV; on the other hand, there were 129 HR HPV-positive patients out of 570 negative or low-risk Pap smear results. Conclusions: It assumed that there is no superiority for HPV genotyping over cytology or vice versa in detecting high-risk patients for cervical cancer; as only 26.8% of women with HPV show abnormal cytology; and from those with normal cytology, 17.9% were positive for HR HPV.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.088
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.017
GPT teacher head0.386
Teacher spread0.370 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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