Bibliographic record
Abstract
Objective To synthetically evaluate the risk factors related to cervical cancer of Chinese women.Methods Published studies concerning risk factors and cervical cancer about Chinese women were systemically searched and assessed by NOS(Newcastle-Ottawa Scale) items from January 1990 to June 2011.16 studies involving 11,126 women were selected for meta-analysis.Stata10.0 software was used for data analysis and for calculating OR and its 95% CI of every risk factor.Results According to NOS items,6 studies were classified as level A and 10 studies were evaluated as level B.Among the 16 analyzed factors,12 factors had statistical significances.Gestation-related factors were as follows: gravidity≥3,OR=2.384(95% CI:1.659-3.425);parity≥3,OR=2.265(95% CI:1.669-3.074);number of abortions≥3,OR=3.713(95% CI:2.470-5.581);and age at first pregnancy≤21 years,OR=2.390(95% CI:1.731-3.225).Sexual behavior-related factors were as follows: marriage times≥2,OR=2.522(95% CI: 1.714-3.713);age at first sex≤20 years,OR=3.467(95% CI: 2.456-4.893);number of sexual partners≥3,OR=2.539(95% CI:1.613-3.996).Gynaecological disease-related factors involved: a history of sexually transmitted diseases(STDs),OR=5.861(95% CI:1.048-13.67);with a history of gynecological diseases,OR=4.807(95% CI:2.899-7.971);education years≤9 years,OR=3.536(95% CI: 2.204-5.672);active and passive smoking,OR=3.055(95% CI:2.435-3.833);and place of resident,OR=2.134(95% CI: 1.010-4.509).Conclusion Many factors are closely related to cervical cancer in fertile women in China.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.062 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".