Reply
Notice bibliographique
Résumé
To the Editor: We appreciate the comment by Dr. Mortezavi and colleagues describing COVID-19 vaccine response and the frequency of disease worsening in patients receiving tofacitinib. The ACR COVID-19 Vaccine Clinical Guidance Task Force was aware of the 2 studies cited and appreciate their summary of the results. We would point out that in the rheumatoid arthritis study by Winthrop et al (1), patients receiving tofacitinib in Study A had a lower likelihood of a satisfactory response to pneumococcal vaccination (45.1%) compared to placebo-treated patients (68.4%), a difference of 23.3% (95% confidence interval [95% CI] −36.6, −9.6%). The differences were numerically even larger for patients receiving concomitant tofacitinib and methotrexate (31.6% of patients with a satisfactory response, difference of −30.2% [95% CI] −47.3, −11.4%) compared to methotrexate monotherapy. Our challenge was in considering the appropriateness of extrapolating results from vaccine studies of influenza, pneumococcal, and tetanus toxoid vaccines to make inferences regarding the anticipated response to vaccination against SARS–CoV-2, a novel antigen to which most individuals have not previously been exposed. The Task Force recognized that infection rates, and perhaps response to vaccinations against those infections, might be heterogeneous according to pathogen. For example, JAK inhibitors approximately double the incidence of herpes zoster compared to biologics such as tumor necrosis factor inhibitors, yet they do not meaningfully increase rates of other infections (e.g., pneumonia) (1-3). We noted that in the Oral Strategy study, adalimumab-treated patients receiving vaccination with the live herpes zoster vaccine had lower incidence rates of herpes zoster (0.0 per 100 patient-years) compared to non-vaccinated patients (incidence rate 2.1 per 100 patient-years) (4). In contrast, and recognizing that numbers were small, tofacitinib-treated patients had similar rates of herpes zoster regardless of vaccination (incidence rate 3.0 per 100 patient-years in vaccinated versus 2.2 per 100 patient-years in unvaccinated patients). We also appreciate the data provided by Dr. Mortezavi and colleagues regarding the rate of disease worsening in patients whose treatment with tofacitinib was briefly interrupted. At ~ 2 weeks, the mean worsening in the 4-variable DAS28 of 0.7 units was of smaller magnitude than typically considered the minimum clinically important difference (MCID) for the DAS28 (i.e., >1.2 units) (5). The MCID for defining disease worsening using the CDAI in patients who had moderate disease activity at the start of treatment is undefined, although a 1-unit change in each of the 4 CDAI components (tender joint count, swollen joint count, patient global, and physician global) is often considered to be the measurement error for each of these (6). Taken together, the mean amount of disease worsening associated with brief interruptions in therapy seems small and likely not of clinical importance for most patients, especially in light of the guidance recommending that JAK inhibitors be withheld for 1 week at the time of each vaccine administration, rather than for 2 consecutive weeks. Ultimately, we await prospective data regarding the influence of JAK inhibitors and other immunomodulatory therapies used at the time of COVID-19 vaccination on immunogenicity and correlates of serologic protection. Since the ACR COVID-19 Vaccine Guidance is a living document, our plan is to rapidly update it and incorporate new evidence as it accumulates.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,023 | 0,031 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,034 | 0,027 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».