Survey of the Impact of COVID-19 on Chronic Liver Disease Patient Care Experiences and Outcomes
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic has a secondary impact on the health of patients with chronic liver disease (CLD). Our objective was to study this impact on care provision, telemedicine, and health behaviours in CLD patients. Methods CLD patients of an urban gastroenterology clinic who attended a telemedicine appointment between March 17, 2020 and September 17, 2020, completed an online survey on care delays, health behaviours, and experience with telemedicine. Chart review was conducted in 400 randomly selected patients: 200 charts from during the pandemic were compared to 200 charts the previous year. Data were extracted for clinicodemographic variables, laboratory investigations, and clinical outcomes. Results Of 399 patients invited to participate, 135 (34%) completed the online survey. Fifty (39%) patients reported 83 care delays due to the COVID-19 pandemic, with the majority (71%) of delays persisting beyond 2 months. Ninety-five (75%) patients were satisfied with telemedicine appointments. There was a longer delay between lab work and appointments in patients seen during the pandemic compared to 2019 (P = 0.01). Compared to the year prior, during the COVID pandemic, there was a similar number of cases of cirrhosis decompensation (n = 26, 13% versus n = 22, 11%) and hospitalization (n = 12, 6% versus n = 5, 3%). Conclusion The COVID-19 pandemic has led to care delays for CLD outpatients, with most delays on the scale of months. These patient-reported experiences and clinical observations can direct optimization of CLD care as effects from the pandemic evolve.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".