Bibliographic record
Abstract
TOTHE EDITOR —We thank Mines and Novelli [1] for their comments on our recent report [2] describing the association between the use of disease-modifying antirheumatic drugs and the risk of developing active tuberculosis (TB). The authors suggest that we probably misclassified a substantial number of patients who did not have active TB as having TB. We had, in fact, already acknowledged this limitation in our report—because we were unable to validate the TB diagnosis in the database, we thus recognized that misclassification could occur. The issue is whether and how such misclassification of cases affects the estimated rate ratio. If the misclassification is similar across the various classes of drugs, it would only tend to attenuate the association towards the null value. If, as suggested by Mines and Novelli, disease-modifying antirheumatic drug users were more likely to be screened for latent TB infection and reported as having active TB, we should have observed a trend in TB diagnosis over the study period when biological disease-modifying antirheumatic drugs were introduced in the United States in 1998, when the potential risk of TB with these therapies was published in 2001 [3], and when recommendations for systematic screening for TB were released in 2002 [4]. There was, however, no increasing trend observed in reported TB cases in our study cohort. Alternatively, we found no time trend in the proportion of reported TB cases among disease-modifying antirheumatic drug users and non—disease-modifying antirheumatic drug users during the study period.
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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 | 0.010 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.031 | 0.047 |
| 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".