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Record W2601268179 · doi:10.14740/cii.v2i1.18

Study of Cervical Pap Smears in 558 Cases

2017· article· en· W2601268179 on OpenAlexvenueno aff
Abu Khalid Muhammad Maruf Raza, Sumia Ahmed Tazri

Bibliographic record

VenueClinical Infection and Immunity · 2017
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSquamous intraepithelial lesionBethesda systemPap smearsGynecologyCervixVaginal smearCervical cancerPap testObstetricsBasal cellCervical intraepithelial neoplasiaCytologyPathologyCancerCervical cancer screeningInternal medicine

Abstract

fetched live from OpenAlex

Background: Cervical cancer is one of the major causes of mortality among women worldwide. The present research aimed to study and analyze 558 pap smears from women presenting with various gynecological indications and as a routine screening test. Methods: This study was carried out in the Department of Pathology of Jahurul Islam Medical College from the pap smears coming from the Gynecology OPD from February 9, 2015 to February 11, 2016. Pap smears were taken from patients aged 20 - 69 years presenting with different gynecological complaints and as a routine screening test using Ayres spatula. Smears were reported as per the 2004 Bethesda System. Results: Of the 558 pap smears studied, 532 smears were satisfactory for evaluation. Among them, 492 (92.5%) were inflammatory smear, 16 (3%) smears showed low grade squamous intraepithelial lesion (LSIL), four (0.8%) smears showed high grade squamous intraepithelial lesion (HSIL), and four (0.7%) smears showed atypical cells suggestive of squamous cell carcinoma. Chronic per vaginal whitish discharge with itching was the most common complain (70.2% patients). Conclusion: Pap smear is an easy and economical screening method to detect premalignant and malignant lesions of cervix which helps in proper treatment. Clin Infect Immun. 2017;2(1):5-7 doi: https://doi.org/10.14740/cii18w

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)0.002
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 score0.067
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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)0.0010.000

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 head0.335
GPT teacher head0.549
Teacher spread0.213 · 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
Published2017
Admission routes1
Has abstractyes

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