THE AUTHORS REPLY
Bibliographic record
Abstract
We thank Dr. Dórea for his interest in our article (1). The objective of our study was to identify the independent effects of 47 potential predictors, measured during pregnancy and after birth, on the incidence of childhood asthma (2). As we acknowledged in the Discussion section of our paper, despite the extent of the data collection efforts and the fact that 47 potential determinants were considered in the analyses, information on some potential risk factors was not available and thus those factors could not be evaluated. As Dr. Dórea highlighted (1), variations in vaccine formulation and immunization schedules have been seen over time in Quebec (3). We agree that the effect of vaccines and their constituents would have been interesting to evaluate. However, immunization programs in Quebec allow children to receive a certain number of vaccines free of charge, and these programs are not covered through the same mechanism as prescribed medications or medical services. Therefore, this information was not captured in the databases used in our study, thereby preventing investigation of the effect of individual or combined vaccines or the presence of thimerosal as a potential determinant of childhood asthma.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".