Symptomology following COVID-19 among adults in Alberta, Canada: an observational survey study
Bibliographic record
Abstract
OBJECTIVE: Fatigue, headache, problems sleeping and numerous other symptoms have been reported to be associated with long COVID. However, many of these symptoms coincide with symptoms reported by the general population, possibly exacerbated by restrictions/precautions experienced during the COVID-19 pandemic. This study examines the symptoms reported by individuals who tested positive for COVID-19 compared with those who tested negative. DESIGN: Observational study. SETTING: The study was conducted on adult residents in Alberta, Canada, from October 2021 to February 2023. PARTICIPANTS: We evaluated self-reported symptoms in 7623 adults with positive COVID-19 tests and 1520 adults who tested negative, using surveys adapted from the internationally standardised International Severe Acute Respiratory and emerging Infection Consortium (ISARIC)-developed COVID-19 long-term follow-up tools. These individuals had an index COVID-19 test date between 1 March 2020 and 31 December 2022 and were over 28 days post-COVID-19 testing. PRIMARY OUTCOME MEASURES: The primary outcomes were to identify the symptoms associated with COVID-19 positivity and risk factors for reporting symptoms. RESULTS: Fatigue was the top reported symptom (42%) among COVID-19-positive respondents, while headache was the top reported symptom (32%) in respondents who tested negative. Compared with those who tested negative, COVID-19-positive individuals reported 1.5 times more symptoms and had higher odds of experiencing 31 out of the 40 listed symptoms during the postinfectious period. These symptoms included olfactory dysfunction, menstruation changes, cardiopulmonary and neurological symptoms. Female sex, middle age (41-55 years), Indigeneity, unemployment, hospital/intensive care unit (ICU) admission at the time of testing and pre-existing health conditions independently predicted a greater number and variety of symptoms. CONCLUSIONS: Our results provide evidence that COVID-19 survivors continue to experience a significant number and variety of symptoms. These findings can help inform targeted strategies for the unequally affected population. It is important to offer appropriate management for symptom relief to those who have survived the acute COVID-19 illness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".