Alberta Childhood COVID-19 Cohort [AB3C, study data contributed to the CITF Databank]
Bibliographic record
Abstract
Background: Given the novel SARS-CoV-2 coronavirus pandemic, there was a need to understand COVID-19 in Alberta children to inform potential treatments, vaccines, and public safety decisions such as when and how children should return to school. This study was not funded by the CITF; Dr. Kellner, as co-chair of the CITF Field Studies and Pediatric working groups, helped design the CITF Core Data Elements and questionnaires. Aims of the original study: The AB3C study aimed to determine how many children with and without known COVID-19 infections have made an immune response (blood antibodies) over time, and the epidemiologic and immunological correlates of infection risk and COVID disease severity. Methods: Parents were invited to enroll their child (less that 18 years) in the study if they lived in or near Calgary, AB. They were enrolled as single-visit or longitudinal participants. In addition to questionnaire items, at each visit (6 months apart) past COVID infection status was ascertained (priority to PCR result) and a blood sample drawn for serology. IgG antibodies to SARS-CoV-2 nucleocapsid protein and receptor binding domain of the spike were measured. Summary of the contributed data: The dataset includes 1030 participants who completed a baseline visit between August and November 2020 and provided at least one blood sample. 94% of participants were followed longitudinally up to 2 years (median: 19 months). Available variables include data for the following areas of information: demographics (age, sex, race-ethnicity and indigeneity, FSA), chronic health conditions, and BMI at baseline; acute COVID infection evidence, SARS-CoV-2 vaccination, and serology over time.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 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".