Did aetiology matter in illness duration and complications in patients presenting in primary care with acute respiratory tract infections early in the COVID-19 pandemic: An observational study in nine countries
Bibliographic record
Abstract
Background Despite considerable research into COVID-19 sequelae, little is known about differences in illness duration and complications in patients presenting in primary care with symptoms of acute respiratory tract infections (RTI) that are and are not attributed to SARS-CoV-2 infection.Objective To explore whether aetiology impacted course of illness and prediction of complications in patients presenting in primary care with symptoms of RTI early in the COVID-19 pandemic.Methods Between April 2020-March 2021 general practitioners from nine European countries recruited consecutively contacting patients with RTI symptoms. At baseline, an oropharyngeal-nasal swab was obtained for aetiology determination using PCR after follow-up of 28 days. Time to self-reported recovery was analysed with Kaplan-Meier curves. Predictors (baseline variables of demographics, patient and disease characteristics) of a complicated course (composite of hospital admission and persisting signs/symptoms at 28 days follow-up) were explored with logistic regression modelling.Results Of 855 patients with RTI symptoms, 237 (27.7%) tested SARS-CoV-2 positive. The proportion not feeling fully recovered (15.6% vs 18.1%, p = 0.39), reporting being extremely tired (9.7% vs 12.8%, p = 0.21), and not having returned to usual daily activities (18.1% vs 14.4%, p = 0.18) at day 28 were comparable between SARS-CoV-2 positive (n = 237) and negative (n = 618) groups. However, among those feeling fully recovered (SARS-CoV-2 positive: 200 patients, SARS-CoV-2 negative: 506 patients), time to full recovery was significantly longer in SARS-CoV-2 patients (10.6 vs 7.7 days, p < 0.001). We found no evidence that predictors of a complicated course differed between groups (p = 0.07).Conclusion Early in the pandemic, the proportion of patients not feeling fully recovered by 28 days was similar between SARS-CoV-2 positive and negative patients presenting in primary care with RTI symptoms, but it took somewhat longer for SARS-CoV-2 patients to feel fully recovered. More research is needed on predictors of a complicated course in RTI.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".