Bibliographic record
Abstract
To the Editor: Harty and Veale have confirmed our finding of higher tender joint counts and overall DAS scores in ever-smokers compared with never-smokers in patients with RA receiving anti-TNF therapy1. However, in contrast to our findings, they also suggest that ever-smokers have marginally higher mean swollen joint counts than never-smokers at both 3 and 12 months. Unfortunately, they do not show the standard deviations and p values, so it is difficult to assess the variability and significance of the data. In fact our own data suggested that the median swollen joint count was marginally higher in ever-smokers than never-smokers after 3 months on anti-TNF therapy, although the difference did not achieve significance (4.5 vs 4.0, respectively; p = 0.2). It is therefore quite likely that there is a small difference in swollen joint counts between ever-smokers and never-smokers receiving anti-TNF, but because of the small size of the effect and the variability in the data a large … Address correspondence to Dr. D.L. Mattey, Staffordshire Rheumatology Centre, Haywood Hospital, High Lane, Burslem, Stoke-on-Trent, Staffordshire, England, ST6 7AG. E-mail: Derek.Mattey{at}uhns.nhs.uk
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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.020 | 0.024 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".