Roadmap to resuming care for liver diseases after coronavirus disease‐2019
Bibliographic record
Abstract
The global pandemic of coronavirus disease-2019 (COVID-19) has led to significant disruptions in healthcare delivery. Patients with chronic liver diseases require a high level of care and are therefore particularly vulnerable to disruptions in medical services during COVID-19. Recent data have also identified chronic liver disease as an independent risk factor for COVID-19 related hospital mortality. In response to the pandemic, national and international societies have recommended interim changes to the management of patients with liver diseases. These modifications included the implementation of telehealth, postponement or cancelation of elective procedures, and other non-urgent patient care-related activities. There is concern that reduced access to diagnosis and treatment can also lead to increased morbidity in patients with liver diseases and we may witness a delayed surge of hospitalizations related to decompensated liver disease after the COVID-19 pandemic has receded. Therefore, it is paramount that liver practices craft a comprehensive plan for safe resumption of clinical operations while minimizing the risk of exposure to patients and health-care professionals. Here, we provide a broad roadmap for how to safely resume care for patients with chronic liver disease according to various phases of the pandemic with particular emphasis on outpatient care, liver transplantation, liver cancer care, and endoscopy.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".