Bibliographic record
Abstract
ORIGINAL ARTICLE, p 1145 Since immunosuppressive monoclonal medications were introduced for the treatment of psoriasis, the focus of safety concerns has shifted. Widely used and ‘time‐honoured’ treatments such as ultraviolet radiation and methotrexate were considered to be understood, and safety issues were discussed with confidence. However, the immunosuppressive effects of therapy were not considered in most discussions. The environment changed. Today when discussing options, including biologics with immunosuppressive effects, a dermatologist can identify with Dustin Hoffman in a classic scene from the movie Marathon Man.1 Sir Laurence Olivier, the villain, has Hoffman tied to a chair and is about to physically torture him for answers. The question is simply ‘Is it safe?’ At first Hoffman says the question is so vague as to be nonsense and he needs more information. The question is repeated and at one turn the answer is a hasty ‘Yes, it is very safe’. When that fails to satisfy, the answer becomes ‘No, it is very dangerous’. How can we answer the safety question in practice today? The current repertoire of biologic treatments has relatively mild, common side‐effects. So the focus is on understanding the rare and severe, such as concerns over infection and cancer. Historically, the earliest clinical reality was exacerbation of tuberculosis from a latent to an active state, mostly identified with antitumour necrosis factor inhibitors. This observation led quickly to processes to mitigate risk. All patients in our practice who will be considered for systemic therapy are evaluated for latent tuberculosis infection (LTBI). The process is not perfect, but the incidence of tuberculosis exacerbation has apparently decreased dramatically. In this issue of BJD, a survey of multiple clinical trials with ustekinumab helped Tsai et al. answer several important questions.2 Firstly, will ustekinumab, an inhibitor of interleukin 12/23 exacerbate LTBI? The answer may be yes. One unusual case, in which chest imaging was abnormal but surrogate tests for tuberculosis were negative, did not qualify to receive isoniazid and did develop active tuberculosis when treated with ustekinumab.3 Secondly, will a regimen of isoniazid prevent this? Again the answer is in the affirmative. While this validates current practice, the proof of the assumptions should strengthen the resolve of practitioners to be vigilant. Proper studies of safety issues are too few4 and ethical concerns abound.5 It is heartening to see a study that addresses key clinical issues and provides a clear, practicable context. This reminds us that safety is a process, not a question. So, ‘Is it safe?’ can lead to a manageable clinical strategy to the benefit of patients and practitioners. Conflicts of interest The author has been a consultant and/or speaker for Abbott, Amgen, Astellas, Celgene, Janssen and Leo Pharma.
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 | 0.018 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.098 | 0.100 |
| Insufficient payload (model declined to judge) | 0.009 | 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".