The PREVENT-COVID in Seniors Study: PRospEctiVe EvaluatioN of immuniTy after COVID-19 vaccines in Seniors [PREVENT-COVID-19, study data contributed to the CITF Databank]
Bibliographic record
Abstract
Background: There is evidence that suggests that vaccines are effective against infection, but it is unclear how age and certain antibodies from previous infections influence their level of effectiveness. Aims of the CITF-funded study: This study aimed to compare short-term and long-term immune responses to COVID-19 vaccines among older adults and seniors. Specifically, they aimed to compare the antibody levels, quantity, and function after vaccination by vaccine type and age. Methods: This cohort study enrolled participants in British Columbia who were receiving their first or second dose of the BNT162b2 (Pfizer), mRNA-1273 (Moderna), or ChAdOx1-S COVID-19 vaccine and over the age of 19, pivoting to oversampling older adults (50+) to address high-priority knowledge gaps. Participants answered an online questionnaire at baseline and provided a blood sample via DBS or venipuncture at baseline and at each follow up (7 times at regular intervals). Contributed dataset contents: The datasets include 772 participants who completed baseline surveys between February 2021 and May 2022. 96% of these participants gave one or more blood samples between February 2021 and June 2023. A total of 3564 samples were collected. Variables include data in the following areas of information: demographics (date of birth, sex, gender, ethnicity), general health (weight and height, flu vaccine, medical conditions), SARS-CoV-2 vaccination, and serology (antibodies against SARS-CoV-2 RBD, nucleocapsid, and spike proteins).
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".