Understanding symptoms suggestive of Long COVID Syndrome and healthcare use among community-based populations
Bibliographic record
Abstract
Context: Long COVID Syndrome (LCS), defined as symptoms for ≥3 months, can include a variety of symptoms such as fatigue, cognitive impairment, shortness of breath, and headaches. Estimates of LCS have relied on medical records which may underestimate true prevalence. Objective: This study aimed to characterize populations with COVID-19 and LCS, describes symptoms, and health care utilization by presenting symptoms. Study Design and Analysis: Two survey’s captured acute COVID-19 symptoms, LCS symptoms, and health care utilization. Descriptive and bivariate analysis assessed populations that did, and did not, access health care based on LCS symptoms. Setting or Dataset: One Survey was disseminated to all residents in Manitoba, Canada, a second to Manitobans accessing a medical fitness center (MFC). Population Studies: In March 2022, members of a MFC were surveyed. Between June-October 2022 an online survey was advertised to all Manitobans using social media, traditional media, and poster distribution. Intervention/Instrument: The survey included 23 questions (7 on COVID-19, 4 on LCS, 7 on health service utilization and 4 demographic). Outcome Measures: Characteristics of patients with COVID-19, LCS symptoms, and healthcare access. Results: In total, we received 921 survey responses. There were 267 responses from the MFC and 654 online responses. Among MFC respondents, 130 (48.7%) reported experiencing LCS. Online 334 (54.2%) respondents reported LCS symptoms. Despite LCS symptoms, only half of respondents accessed primary care (MFC 57.5%, online 63.2%). Among online respondents, 15.2% accessed an ED, and 32.0% accessed a specialist or therapist. Symptoms associated with primary care access included extreme fatigue (MFC 56.5% online 95.9%), shortness of breath (MDC 60.9%, online 87.8%), cognitive impairment (MFC 52.2%, online 77.0%), and headaches (MFC 39.1%, online 92.6%). Online respondents reported shortness of breath was the most common reason for accessing the ED (90.5%). The majority of online respondents that saw a specialist or therapist (89.7%) reduced their activities due to symptoms and 20.6% required assistance with day-to-day activities. Conclusions: The survey captured experiences of patients with and without health system use and confirms that health records data underrepresents COVID-19 and LCS. The variety of symptoms experienced presents a challenge for health care providers demonstrating the value of interdisciplinary care teams.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".