Identification of risk factors associated with cervical Intra-epithelial Neoplasia among women in British Columbia
Bibliographic record
Abstract
Multiple etiologic factors have been described for invasive cervical cancer. The most important ones being sexual activity and smoking. Less is known regarding the factors predisposing to risk of Cervical Intraepithelial Neoplasia (CIN). The increasing incidence among women prompted a study of this disease in British Columbia in carrying out a case control study to identify the risk factors associated with the disease. Incidentally, that is the main focus of this paper. A case-control design was used with cases and controls identified from the Cytology database of the British Columbia Cancer Agency which contains a complete record of all cervical cytology done in British Columbia. Cases were women with diagnosis of cervical dysplasia or carcinoma in-situ whereas controls were women with no history of cervical abnormality. Estimates of the relative risk together with its 95% confidence interval are obtained from the maximum likelihood estimates of the binary logistic regression models. The important risks factors associated with CIN that have been identified in this study are current cigarette smoking, sexual frequency, number of different lifetime sexual partners, combine usage of both condom and diaphragm and dietary intake of vitamin A.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".