Tyas et al. Respond to "Predictors of Rate of Change in Disease Progression"
Bibliographic record
Abstract
In her commentary (1), Dr. Glymour describes four phenomena leading to a spurious association between risk factors for disease onset and the rate of disease progression. We agree with Dr. Glymour that the one that is relevant to our study (2) is the issue of beginning observations in the middle of a developing pathologic process. The inability to begin observation at the initiation of a pathologic process is, of course, not unique to this study but instead challenges all studies of dementia as well as many other conditions. This issue is important to our study (2) because a number of our participants were diagnosed with dementia at their first assessment. Because we examined predictors of transitions in cognitive status across the trajectory from intact cognition to dementia, dementia was treated as an absorbing state (endpoint). Those women who already had dementia at the beginning of the study were thus excluded from our analyses. Contrary to Dr. Glymour's argument (1), however, we do not agree that this necessarily leads to spurious associations. Instead, our analyses found the opposite: adjusting for baseline status increased the odds ratios, indicating that our reported results of the effects of the covariates are, in fact, conservative.
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 | 0.021 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.037 | 0.028 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".