Bibliographic record
Abstract
BACKGROUND As COVID-19 vaccines develop, methods for identifying vulnerability within groups to prioritized vaccination remain un-established. This paper presents a novel approach based on population-based analysis of viral pneumonia vulnerability, as an example. METHODS The analysis employed an anonymous, 16-year, population dataset (n = 768,460) consisting of International Classification of Diseases (ICD-9) diagnoses, demographics, and dates identifying those with viral pneumonia and permitting linkage of these individuals to all their associated diagnoses for calculation of odds ratios and proportions of disorders before and after the index viral pneumonia diagnosis. RESULTS Females and males had results of differing magnitude. For those with viral pneumonia, the mean number of diagnoses was greater in both the subsample and whole sample, with associated diagnoses arising about 4 years on average before the viral pneumonia index diagnosis. Within the subsample, compared to those without, the temporal analysis revealed distinct over-representation for those with viral pneumonia at visit one and over the first fifty visits. Further, those with viral pneumonia had diagnoses not represented in the group without viral pneumonia. CONCLUSIONS The population-based analysis of temporal hyper-morbidity may be a viable and economical approach to identifying viral pneumonia vulnerability. The approach presented in this paper may provide an economical means of identifying vulnerability to COVID-19 in regions where comparable data are available for analysis. Rational approaches may optimize vaccination and help to limit the spread of the disease and to some extent alleviate the health service burden.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".