Two‐year trajectories of COVID‐19 symptoms and their association with illness perception: A prospective cohort study in Amsterdam, the Netherlands
Bibliographic record
Abstract
Background: We used data from a prospective cohort to explore 2-year trajectories of 'long COVID' (persistent symptoms after SARS-CoV-2 infection) and their association with illness perception. Methods: RECoVERED participants (adults; prospectively enrolled following laboratory-confirmed SARS-CoV-2 infection, May 2020-June 2021) completed symptom questionnaires at months 2-12, 18 and 24, and the Brief Illness Perception Questionnaire (B-IPQ) at months 1, 6 and 12. Using group-based trajectory models (GBTM), we modelled symptoms (mean total numbers and proportion with four specific complaints), including age, sex, BMI and timing of infection as covariates. In a multivariable linear mixed-effects model, we assessed the association between symptom trajectories and repeated B-IPQ scores. Results: Among 292 participants (42% female; median age 51 [IQR = 36-62]), four trajectories were identified, ranging from Trajectory 4 (8.9%; 6 + symptoms) to Trajectory 1 (24.8%; no symptoms). The occurrence of fatigue and myalgia increased among 23% and 12% of participants, respectively. Individuals in Trajectory 4 experienced more negative adjusted B-IPQ scores over time than those in Trajectories 1-3. Conclusions: We observed little fluctuation in the total number of symptoms, but individual symptoms may develop as others resolve. Reporting a greater number of symptoms was congruent with more negative illness perception over time.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".