Peer Review #2 of "Prevalence and type distribution of human papillomavirus in a Chinese urban population between 2014 and 2018: a retrospective study (v0.1)"
Bibliographic record
Abstract
BackgroundHuman papilloma virus (HPV) infection is the most common sexually transmitted infections among women worldwide.The current study's main objective is to report the prevalence and distribution of HPV types in an urban population in Beijing, China. MethodsAll the eligible female participants aged≥18 years were recruited from the Aerospace Center Hospital in Beijing, China between 2014 and 2018.A total of 21 HPV types were detected by the polymerase chain reaction (PCR) reverse dot blot method and fluorescence quantitative PCR method. ResultsIn total, 12 high risk HPV types and nine low risk HPV types were detected.The HPV-positive rates were 8.85% in 2014, 7.16% in 2015, 7.60% in 2016, 8.31% in 2017, and 7.72% in 2018, respectively, in an urban population in Beijing, China.Overall, no significant differences in the HPV-positive rates were found over the five years.The peak prevalence of HPV infection in all types was observed in age group of 20-24 in all types.HPV52 was the dominant HPV type across the five years.Among all 21 HPV types, HPV66, HPV26, and HPV59 were ranked the top three in coinfection occurrence. ConclusionsOur findings are great helpful for HPV screening and vaccination., and the associations between gynaecological diseases and the HPV types with high prevalence, particularly HPV52, warrant further investigation.
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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 | 0.008 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.073 | 0.024 |
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".