Bibliographic record
Abstract
The thesis focuses on the application of epidemiological and biostatistical methods for better test accuracy assessment in infectious diseases, particularly in the pediatric population and in population health surveillance. We explore and propose a modelling approach that includes clinical and/or population health relevant constructs in the assessment of test accuracy and apply this to the respiratory pathogen SARS-CoV-2. We examine the severity of the multi-inflammatory syndrome in children and the challenges in diagnosing past infections during the early pandemic. We provide seroprevalence estimates and risk factors for seropositivity from a prospective cohort study in Belgian schools. Additionally, we report on the development, initiation, and analysis of the data provided by the surveillance system of Flemish school COVID-19 cases. Throughout the thesis, we use Bayesian latent class analysis to estimate the sensitivity and specificity of tests to diagnose SARS-CoV-2 past-infection rates. We then improve the more classically used latent class model for the assessment of the prevalence and the accuracy estimates, via the decomposition of the test accuracy question into its elements i) the tests under evaluation, ii) their measurands and iii) the target condition(s). We use directed acyclic graphs to visualize the model and Bayesian inference to obtain the estimates. We find more realistic estimates for the accuracy of the tests with regards to the measurands and the tests with regards to the different target conditions, clarifying their clinical performance to diagnose cases depending on the moment of testing. This corroborates the importance of explicitness regarding the target condition.
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 | 0.093 | 0.330 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".