What are the Clinical-Epidemiological Differences between Initial Infection and Reinfection Covid-19 with Fourth Dose of Bivalent mRNA Vaccine? A Study in the Period from October 2022 to October 2023, In a General Medicine Office (Toledo, Spain)
Bibliographic record
Abstract
Background: The clinical-epidemiological differences between primary infections versus reinfections of covid-19 with the 4th dose of bivalent mRNA vaccine are not known. Objective: Compare primary infections versus reinfections of covid-19 with 4th dose of bivalent mRNA vaccine. Methodology: An observational, longitudinal and prospective case series study of adult patients with covid-19 infections in vaccinated people with 4th dose in general medicine from October 1, 2022 to October 1, 2023. Descriptive epidemiological analysis considered a set of selected demographic and clinical features. Results: 16 people with fourth dose and with covid-19 infections and 5 people with fourth dose and with covid-19 reinfections from October 2022 to October 2023 were included. Reinfection versus primary infections were more frequent in women, socialhealth care workers, complex family, moderate-severe severity of infection, chronic diseases of the Neoplasms, Endocrine, Musculo-skeletal and Genitourinary groups, and presented more ENT and neurological symptoms. But, the only variable with statistically significant differences was the presence of Neoplasms (13% versus 0%; Fisher exact test = 0.0483). Conclusion: In the context of a general medicine consultation in Toledo (Spain) from October 2022 October 2023, in people with the fourth dose of bivalent mRNA vaccine against covid-19, covid-19 reinfections did not differ in their clinicalepidemiological characteristics except for being more frequent in presumably immunosuppressed patients (patients with Neoplasms) compared to covid-19 primary infections. In any case, our results, although consistent with the existing literature, should be taken with caution due to the small number of covid-19 cases included.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".