Concurrent Infections With Human Papillomavirus and Cervical Intraepithelial Lesions: What Is the Relationship?
Bibliographic record
Abstract
Background: Human papillomavirus (HPV) infection is necessary for cervical dysplasia and cervical cancer to develop, but infection with HPV is not predictive of which women will develop cervical squamous intraepithelial lesions (SILs) or cancer. This study examines the relationship between the number of concurrent HPV infections and risk of SIL as well as the variations in HPV types in a diverse population. Methods: IRB approval was obtained. Women presenting for gynecologic exam were recruited to participate. ThinPrep samples were sent for cytological evaluation, and cervical cells were obtained for HPV screening and typing (INNO-LIPA genotyping kit); medical information was recorded into a Microsoft Access database. Data analysis was performed using JMP statistical software. Results: Seven hundred nineteen women were recruited to participate; race/ethnic distribution was 79.6% for African-American/Black and 14.2% for Caucasian/White with an average age of 31.4 years. Of the patients, 27.5% were HPV-positive, and the average number of HPV types present at the time of the Pap test was 2.55. There was no difference in the number of concurrent HPV infections when stratified via race/ethnicity and cervical cytology/pathology. Regardless of race/ethnicity and cytology and pathology, the three most common high-risk types of HPV were 52, 16, and 39. Conclusions: Abnormal cytology/pathology did not vary with number of concurrent HPV infections. J Clin Gynecol Obstet. 2017;6(2):29-33 doi: https://doi.org/10.14740/jcgo437w
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".