Additional file 1 of Differences in clinical characteristics and quantitative lung CT features between vaccinated and not vaccinated hospitalized COVID-19 patients in Italy
Bibliographic record
Abstract
Additional file 1: Figure S1. Study Flowchart. Figure S2. Prevalence of vaccination by month of admission 2021. Table S1. Outcomes of not vaccinated and fully vaccinated patients. Table S2. Maximum Respiratory Support during hospitalization of not vaccinated and fully vaccinated patients. Table S3. Maximum Respiratory Support during hospitalization of survivors and not survivors. Table S4. Lung CT scan quantitative data after linear regression model adjusted for age, month of admission, presence of chronic lung diseases, ethnicity and sex (A) overall for January–December 2021 and (B) Only for patients admitted in July–December 2021. Figure S3. Underlying assumptions regarding the causal links between variables for estimating the: (A) effect of COVID-19 vaccination on lung CT-scan parameters; (B) effect of COVID-19 vaccination on in-hospital death; (C) effect of lung CT-scan parameters on in-hospital death. Figure S4. Histograms of fraction of total lung weight. Table S5. Unadjusted and adjusted HR from fitting a standard Cox regression model Of in hospital death by (A) total lung tissue mass strata, (B) Lung gas volume strata, (C) Not aerated plus poorly aerated fraction of total lung weight, (D) Vaccination status (January–December 2021), (E) Vaccination status (only July–December 2021). Table S6. Probability of admission in ICU in vaccinated and non-vaccinated COVID-19 patients has been investigate by means of logistic regression model crude and adjusted for age, Charlson Comorbidity Index, PaO2/FiO2 at admission and period of admission. Table S7. Clinical Characteristic, lung CT scan quantitative data, respiratory function maximum respiratory support of not vaccinated patients admitted or not admitted in ICU. Figure S5A. Fully vaccinated and Not vaccinated Kaplan-Meier survival curves. B. Age strata KM survival curves survival.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.731 | 0.068 |
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".