Is Vaccination Acting As a Placebo in Preventing Symptoms of Long Coronavirus Disease 2019?
Bibliographic record
Abstract
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?
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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 | 0.012 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.008 |
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".