Is Vaccination Acting As a Placebo in Preventing Symptoms of Long Coronavirus Disease 2019?
Notice bibliographique
Résumé
To theEditor—Nehme et al reported that having any symptom(s) 12 weeks after Omicron infection occurred in 11.7% (95% confidence interval [CI], 11.4–12.0) compared with 10.4% (95% CI, 9.9–10.8) in test-negative controls (Nehme et al’s Table 2; P < .001), an adjusted difference in having long coronavirus disease 2019 (COVID-19) of only 1.3% [1]. Statistically significant differences in individual symptoms occurred only for insomnia (by 0.3%), loss/change in smell (by 0.8%), and loss/change in taste (by 0.4%) [1]. There was no statistically significant difference in functional impairment at 12 weeks between cases and test-negative controls. This suggests that many long-COVID cases would not have met the World Health Organization consensus definition of long COVID, which states that symptoms “generally have an impact on everyday functioning” [1, 2]. We believe these results emphasized the importance of having a control group in studies of long COVID and question the narrative that long COVID is a common and feared complication of COVID-19 infection. Nehme et al also reported in their subanalyses that the prevalence of symptoms in vaccinated vs unvaccinated Omicron cases was statistically significantly different at 9.7% vs 18.1% (P < .001) and that, considering only nonvaccinated individuals, there was no adjusted statistically significant difference in the prevalence of symptoms between Omicron cases and test-negative controls (P = .100) [1]. These subanalyses were asserted to suggest a benefit from vaccination in preventing long COVID. However, there was a glaring omission that does not support the claimed benefit of vaccination in preventing long COVID. A direct subanalysis comparing vaccinated cases and vaccinated test-negative controls was required in order to determine if vaccination was protective against long COVID, yet this was not reported. Indeed, in Nehme et al's Figure 2, it appears that vaccination was associated with a larger benefit in test-negative controls than in Omicron cases. The prevalence of any symptom in test-negative controls who were nonvaccinated was 18.9% (compared with 17.8% in the Omicron cases, P = .100) and lower in test-negative controls who were vaccinated (although the proportion is never stated, it was lower than the 9.7% in the vaccinated Omicron cases, as shown in their Figure 2) [1]. This suggested to us that vaccination is acting as a placebo, or vice versa, nonvaccination as a nocebo, in association with symptoms typical of long COVID. Otherwise, how could vaccination protect noninfected test-negative controls (as well as or better than infected test-positive cases) from having symptoms similar to long COVID? Can the authors confirm in an adjusted subanalysis that includes only vaccinated individuals that there were no differences in having any symptom(s) between test-positive Omicron cases and test-negative controls? Finally, we note an error where in Table 2 the number of nonvaccinated test-negative controls was given as n = 229, while in Table 1 this number was given as n = 154. Does this error affect the results of the subanalysis in nonvaccinated individuals?
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 | 0,012 | 0,061 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,006 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,053 | 0,008 |
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 ».