Additional file 1 of Impact of vaccination against severe COVID-19 in the French population aged 50 years and above: a retrospective population-based study
Bibliographic record
Abstract
Additional file 1: Text S1. Estimating the number of directly averted events. Text S2. Reconstructing time series of hospitalizations, ICU admissions and deaths by variant. Table S1. Estimations of vaccine effectiveness by Santé publique France (methodology described in Tamandjou et al., Vaccine 2023, DOI: 10.1016/j.vaccine.2023.02.062). Table S2. Estimations of vaccine effectiveness against hospitalizations in England, among people 65 years and above (Stowe et al., Nature Communications 2022, DOI: 10.1038/s41467-022-33378-7). Table S3. Estimations of vaccine effectiveness against deaths in Canada, among people aged 18 years and above (Buchan et al., JAMA Open Network 2022, DOI: 10.1001/jamanetworkopen.2022.32760). Table S4. Estimated number of averted hospitalizations by age, dose and variant in the French population aged 50 years and above, from week 53-2020 to week 9-2022. Table S5. Estimated number of averted ICU admissions by age, dose and variant in the French population aged 50 years and above, from week 53-2020 to week 9-2022. Table S6. Estimated number of averted deaths by age, dose and variant in the French population aged 50 years and above, from week 53-2020 to week 9-2022. Figure S1. Flowchart of the French vaccinated population aged 50 years and above included in our study, from week 53-2020 to week 9-2022 (VAC-SI database, Santé publique France). Figure S2. Vaccine coverage (A) and proportion of vaccinated people according to the week in which they received their last dose (the last dose at the time of observation), for 4 weeks of observation w (week 6-2021 (B), week 20-2021 (C), week 40-2021 (D) and week 9-2022 (E)). Figure S3. Numbers of hospitalizations (A), ICU admissions (B) and deaths (C) observed and expected without vaccination, in the French population aged 50 years and above, from week 53-2020 to week 9-2022.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Insufficient payload (model declined to judge) Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.169 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".