Bibliographic record
Abstract
To the Editor: We thank Dr. Schou for her insightful comments.1 The interpretation of data from an observational study such as this is complex, and the conclusions are by necessity less robust than in a randomized controlled trial. As noted in our manuscript,2 there were differences in the underlying characteristics between the groups of patients receiving the different treatments at baseline. As Dr. Schou has noted,1 this included the length of follow-up, which is important in determining the stability of the Kaplan-Meier (KM) estimate.3 The follow-up time was calculated as the time from index until the time of last follow-up, censored at the time of the discontinuation for those who discontinued treatment. The median follow-up was estimated using KM methods. This is the “time to censoring” method as recommended by Betensky.3 In particular, golimumab (GOL) is a much more recent entrant onto the … Address correspondence to Dr. H. Griffiths, Barwon Rheumatology Service, 156 Bellerine Street, Geelong, 3220 VIC, Australia. Email: hedley{at}brservice.com.au.
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 | 0.004 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.033 | 0.033 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".