Impact of study design on vaccine effectiveness estimates of 2 mRNA COVID-19 vaccine doses in patients with stage 5 chronic kidney disease
Bibliographic record
Abstract
In observational studies, the World Health Organization recommends the test-negative design (TND) to assess corona virus disease 2019 (COVID-19) vaccine effectiveness (VE), due its accuracy and efficiency.1 The TND is a variation of the case–control design that restricts the study population to individuals who are tested for severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) and have symptoms consistent with COVID-19.2 Although this design helps reduce bias resulting from differences in healthcare-seeking behavior,1 a major barrier when using administrative healthcare data is the requirement for individuals who are tested for SARS-CoV-2 infection to fulfill a specific case definition (i.e., being symptomatic), which requires symptom data to be well recorded.
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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 | 0.507 | 0.675 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.028 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".