Squamous Cell Abnormalities and Risk Factors on 10,979 Pap-Smears in a Tertiary Care Hospital in Trivandrum, South India
Bibliographic record
Abstract
The study aims at assessing squamous abnormalities and risk factors among women visited a tertiary care hospital for gynaecological problems in Trivandrum, south India.Regional Cancer Centre (RCC), Trivandrum, has been conducting a clinic in Women & Children Hospital, Trivandrum, since 2006, where women attended for gynaec problems were referred to routine Pap-smear.Age and reproductive factors were collected.The processing of the smear was done at RCC. Logistic regression analysis was employed to assess the odds ratio (OR) and 95% confidence interval (CI).10,979women had undergone Pap-smear during 2010-2015.Among these, atypical squamous cells of unspecified significance were 2.9%, low grade squamous intra-epithelial lesions, high-grade squamous intraepithelial lesions (HSIL)/cancers were 1.6%.For developing HSIL/cancers women with higher education had OR of 0.20 (CI: 0.07-0.58)compared to women with no education.Women with age at marriage >30 years had OR of 0.23 (CI: 0.08-0.66)for developing HSIL/cancer compared to women with age at marriage <20 years.Women with unhealthy cervix had OR of 3.16 (CI: 1.80-5.54)for having HISL/cancer compared to women with normal cervix.In conclusion, Pap-smear clinics would help to detect women in premalignant conditions and also has a greater role in the diagnosis of inflammatory lesions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".