Survival and safety of treatment with infliximab in the elderly population
Bibliographic record
Abstract
1Department of Rheumatology, Dijon University Hospital, Dijon, 2Department of Medicine and Rheumatology, J. Bouveri Hospital, Montceau les Mines, 3Department of Rheumatology, Hôtel Dieu Hospital, Le Creusot, 4Department of Rheumatology, G. Ramon Hospital, Sens and 5INSERM/ERIT-M 0207, University of Burgundy, Dijon, France Sir, Anti-tumour necrosis factor α (TNF-α) agents, such as infliximab, represent a major advance in rheumatoid arthritis (RA) and ankylosing spondylitis (AS) treatment. Infliximab is usually well tolerated but has some potential adverse effects, particularly infections [1–6]. The ageing process induces a decline in the function and control of the immune system. Thus, the prevalence of adverse effects of anti-TNF-α agents, and particularly of infections, might be increased in the elderly population. However, being elderly does not appear in most recommendations regarding the prescription of anti-TNF-α agents [7, 8]. This might be due to the fact that, to our knowledge, the elderly population has not been evaluated separately, although we have suggested in previous work that the prevalence of severe pyogenic infections might be greater in older than in younger infliximab-treated patients [6], and the mean age of patients developing severe infections was higher than the mean age of the whole group of patients treated with anti-TNF-α agents in Northern Ireland [9]. The aim of the present study was to evaluate the survival and the safety of infliximab in older patients in comparison with younger patients, using infliximab withdrawal and the reason for withdrawal as an outcome.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".