The Race to Block TNF-α: The Caution Flag Is Raised Again
Bibliographic record
Abstract
Serious bacterial infections in patients with rheumatoid arthritis uner anti-TNF-α therapy. Kroesen S, Widmer AF, Tyndall A and Hasler P. Rheumatology 2003;42:617–621. This retrospective study from Basel, Switzerland assessed the incidence rates of severe infections in rheumatoid arthritis (RA) patients before and after receiving anti-tumor necrosis factor alpha (anti-TNF-α) therapy. The authors reviewed the charts of all patients (n = 60) receiving anti-TNF-α therapy at their institution. Of the 60 patients receiving anti-TNF-α therapy, 60% were treated with etanercept and the rest with infliximab. The patients' charts were assessed for serious infections defined as infection requiring hospitalization and/or intravenous antibiotics. Charts were reviewed up to 3 1/2 years after starting anti-TNF-α therapy and all serious infections were recorded. The patients acted as their own controls as the rates of serious infections post anti-TNF-α therapy were compared to rates in the two years preceding the initiation of treatment. The authors indicated that the patients were treated with standard care for rheumatoid arthritis patients both before and after anti-TNF-α therapy.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".