Post-acute sequelae of COVID-19 in residents in long-term care homes: Examining symptoms and recovery over time
Bibliographic record
Abstract
BACKGROUND: Post-COVID-19 condition (PCC) has been studied extensively since the inception of the COVID-19 pandemic. In the population of long-term care (LTC) home residents, however, information about PCC and recovery after the acute phase of COVID-19 is lacking. This study contributes evidence about symptoms over time in 459 residents in nine Canadian LTC homes. METHODS: In a comprehensive retrospective chart review, we recorded medical symptoms in a 4-week period before contracting COVID-19 ("PRE-COVID") and during 24 weeks after contracting infection (a 4-week "ACUTE-COVID" period and five subsequent 4-week periods "POST1-5"). We investigated the number and type of symptoms over time, examined different "recovery trajectories", and compared the characteristics of residents across different trajectories. RESULTS: In the sample overall, the number of different symptoms increased from PRE-COVID to ACUTE-COVID (mean difference of 3 symptoms, p<.001), returning to the PRE-COVID level within the first two months post-infection. An individual-level examination revealed that after ACUTE-COVID about a quarter of residents did not return to their symptom baseline. There was no statistically significant difference in demographic characteristics or PRE-COVID comorbidities across different recovery trajectories. Comparing the group of residents that did not return to their symptom baseline and the group that did, the risk for not returning to baseline increased with the number of symptoms in ACUTE-COVID (adjusted for age, sex, and PRE-COVID comorbidities, exp[B]=1.15, 95% CI [1.05;1.25], p=.002). Additionally, there was a greater increase in the number of symptoms from PRE-COVID to ACUTE-COVID in the former group (significant interaction effect, p<.001). We present symptom types in each time-period. CONCLUSIONS: Group-level results indicated that the number of symptoms after contracting COVID-19 fell to the pre-COVID level within the first two months post-infection. An examination of individual-level symptom trajectories contributed a more granular picture of recovery after infection and characteristics of residents across different trajectories.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".