COVID-19 and excess mortality: Was it possible to lower the number of deaths in Slovenia?
Bibliographic record
Abstract
This paper presents new data on the age structure of hospitalised SARI (severe acute respiratory infection) patients, with or without COVID-19, broken down by gender, place of infection, and region. The leading hypothesis that COVID-19 deaths are overestimated despite the high share of excess deaths was confirmed, bringing to light the important issue of the demographic breakdown of the population at risk. Thus, the main reason for the decreasing number of COVID-19 deaths is to be sought within the exhausted demographic pool of the elderly population in 2020, when the mortality rate was 19% higher compared to the previous five-year period (2015-2019). Demographic disparities across regions are immense and statistically explain the differences in the ?infected versus deceased? ratio. The excess mortality in 2020 was unusually high, but the projected value for 2020 based on the mortality pattern across age groups from 2015 to 2019 contributed up to one-third of the surplus. So, for one-quarter of alleged COVID-19 deaths (roughly 600 out of some 3,300 in 2020), death was expected to take place in 2020 anyway.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".