Are there clinically significant interactions between COVID-19 vaccination and post-COVID-19 condition (long COVID)?
Bibliographic record
Abstract
Background: “Long COVID” has been studied both as post-acute sequelae (PAS), defined as symptoms 4 to 12 weeks post diagnosis, and as post-COVID-19 condition (PCC), defined by the World Health Organization as persistent or recurring symptoms lasting for at least 8 weeks and occurring 12 or more weeks after an acute COVID-19 infection. It is important to know if there are any beneficial or harmful effects of COVID-19 vaccination on PAS or PCC, or if PAS or PCC increases the risk of adverse events following vaccination. This report addresses three questions: Does COVID-19 vaccination before or after COVID-19 infection decrease the risk of developing PAS or PCC? Among those who already have PAS or PCC, does COVID-19 vaccination affect their symptoms? Is it safe to receive a COVID-19 vaccine after PAS or PCC? Methods: Twenty databases and key websites were searched for relevant reviews, peer-reviewed publications and preprints up to January 13, 2022. Search terms included the following: immuniz*, immunis*, vaccin*, long covid, long-covid, post covid, post-covid, chronic covid, chronic-covid, long-term sequelae, long hauler and long-hauler. The search netted 97 citations, which were screened for relevance. Data were extracted from relevant studies into three evidence tables to address each of the questions. Results: Fourteen relevant studies were identified: four prospective cohort studies; four retrospective cohort studies; and six cross-sectional studies. One was peer-reviewed, twelve were preprints and one was a letter to the editor. Twelve studies reported on vaccines authorized for use in Canada and are reported on here; the two others were on a vaccine authorized for use in India.
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 | 0.007 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".