Corrigendum - Global prevalence of post-COVID-19 condition: a systematic review and meta-analysis of prospective evidence
Bibliographic record
Abstract
This corrigendum is being published to correct a number of errors and imprecisions, on pages 113, 120-125 and 138, of the following article: Taher MK, Salzman T, Banal A, Morissette K, Domingo FR, Cheung AM, Cooper CL, Boland L, Zuckermann AM, Mullah MA, Laprise C, Colonna R, Hashi A, Rahman P, Collins E, Corrin T, Waddell LA, Pagaduan JE, Ahmad R, Jaramillo Garcia AP. Global prevalence of post-COVID-19 condition: a systematic review and meta-analysis of prospective evidence. Health Promot Chronic Dis Prev Can. 2025;45(3):112-38. https://doi.org/10.24095/hpcdp.45.3.02 The authors would like to clarify a few points specifically related to the referencing of results from the 2023 Canadian COVID-19 Antibody and Health Survey (CCAHS).Footnote1 These clarifications reflect refinements in how the source data are interpreted and attributed, and do not affect the core findings or conclusions of the review. Bold has been used to identify the changes and updated text.
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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 | 0.028 | 0.322 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.114 | 0.023 |
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".