Effectiveness of Vaccination: Hospital Admission and Length of Stay
Bibliographic record
Abstract
Background COVID-19 vaccinations were first met with public hesitancy. There are some debates about the vaccines’ effectiveness in reducing hospital admissions or length of stay. Objective We aim to compare the effectiveness of different vaccine statuses and types with hospital admissions and length of stay. Methods Data related to hospital admissions, length of stay, the need for intensive care, and vaccination data were obtained from the Jordanian Ministry of Health. Results A total of 17,182 hospital admissions were recorded from February 2, 2021, the earliest date a vaccinated individual who has passed the 20-day mark on the first dose was admitted with relation to COVID-19, to August 15, 2021. The mean age admitted was 53 years. From all those who were admitted, the unvaccinated group was the majority in both overall admissions (93.7% with the length of stay of 6.9 days for older groups and 8.3 days for the younger) and intensive care unit admissions for both the older and younger age groups (91.23% and 93.3%, respectively), followed by those fully vaccinated (3.4% with the length of stay by vaccine type: Pfizer 4.9-6.1 with 115 admissions; AstraZeneca 10.8-5.1 with 26 admissions; Sinopharm 5.3-6.7 with 440 admissions; Sputnik 2-4 with 4 admissions) and those with only the first dose (2.5% with the length of stay by vaccine type: Pfizer 7.05-7.25 with 133 admissions; AstraZeneca 7.73-7.53 with 109 admissions; Sinopharm 6.5-7.9 with 253 admissions; Sputnik 4 with 1 admission). The time between the vaccination and admission was noticeably longer after the second dose of each vaccine compared to only the first dose with the exception of AstraZeneca (Pfizer 35.4-35.73 to 46.8-79.85; AstraZeneca 48.3-50.7 to 33.4-43.4; Sinopharm 22.65-24.86 to 54-62.9; Sputnik 28 to 99.5-101.5). Conclusions The study showed a lower admission and shorter stay at the hospital for those who are vaccinated, indicating the ability of vaccines to reduce the burden on the health care system.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".