Bibliographic record
Abstract
We thank Drs. Basu and Galvani for their observations (1) on our paper (2). However, we disagree with several of their comments. We did not assert that our multiparameter likelihood-based approach was superior to alternative calibration techniques, including Bayesian approaches. Rather, we emphasized the lack of consensus on an ideal approach, credited the “theoretical appeal” of Bayesian methods, and discussed the limitations of our own methods. We suggested that our approach would “appeal to epidemiologists” because of the empirical value of using the full complement of data from a credible cohort study to demonstrate the validity of the model. Van de Velde et al. (3) recently published an elegant analysis of parameter uncertainty in a model of vaccine effectiveness, based on similar principles. We noted in our paper (2) that Bayesian techniques require “meaningful” prior distributions (not “informative” priors, as Basu and Galvani mistakenly claim), to highlight the uncontested view that priors are a critical component of Bayesian methods (4). Indeed, uninformed priors may be “meaningful,” since they can reflect the uncertainty of knowledge at baseline. Basu and Galvani state that our approach “is not capable of discriminating among ‘good-fitting’ parameter sets” (1, p. 983). While we agree that a Bayesian approach can offer “specific criteria… for distinguishing among alternative model structures,” we believe that in view of the biologic complexity of the natural history of human papillomavirus (HPV) infection and cervical cancer, capturing the uncertainty, as we did, is more important than using statistical constructs to discriminate among alternative model specifications.
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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 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.040 | 0.057 |
| Insufficient payload (model declined to judge) | 0.019 | 0.015 |
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".