The Evidence for Efficacy of HPV Vaccines: Investigations in Categorical Data Analysis
Bibliographic record
Abstract
Recent approval of HPV vaccines and their widespread provision to young women provide an interesting context to gain experience with the application of statistical methods in current research. We demonstrate how we have used data extracted from a meta-analysis examining the efficacy of HPV vaccines in clinical trials with students in applied statistics courses at both introductory and intermediate university levels. The data are suitable for various techniques in categorical data analysis including comparison of proportions, analysis of contingency tables, logistic regression and log-linear models. These data are relevant to all young people and, because of their health science context, can be used in courses in biostatistics or the health sciences as they allow for further discussion of metaanalyses and randomized controlled trials. We also discuss how we have used these data to promote discussion of statistical issues such as statistical versus practical significance, independence, and a common misconception involving the interpretation of p-values.
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 | 0.405 | 0.834 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.014 |
| Bibliometrics | 0.013 | 0.025 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".