Effect of coronavirus disease 2019 on diagnosis and treatment of hepatocellular carcinoma: a systematic review
Bibliographic record
Abstract
Aim: Changes in strategies in the coronavirus disease 2019 (COVID-19) crisis and the imposing of restrictions have isolated many vulnerable patients including those with hepatocellular carcinoma (HCC) from routine medical care. This study investigated how the COVID-19 pandemic is affecting the diagnosis and treatment of HCC. Methods: An extensive search was conducted in the PubMed, Scopus, and Web of Science databases by using the appropriate keywords: COVID-19, hepatocellular carcinoma, hepatocellular cancer, and MeSH. Studies in English related to the purpose of the study were included in the analysis, and review studies, case reports, letters to editors, comments, and reports were excluded. The quality of the studies was assessed by the "Adapted Newcastle-Ottawa Quality Assessment Scales" checklist. The Endnote X7 software has been used for managing items. Results: The final qualitative analysis consisted of 27 articles. During the COVID-19 crisis, HCC diagnosis decreased from 20% to 34.13% compared to pre-crisis. The impact of the COVID-19 pandemic on HCC treatment encompasses a wide range of aspects. Generally, delays in treatment for patients with HCC ranged from more than one month for 21.5% of patients in France, to two months for 26% of patients in Italy, up to 30% in Austria, and 66.7% in Asia-Pacific countries. Conclusions: According to the findings, developing and implementing appropriate diagnostic and therapeutic strategies and developing low-cost and high-precision screening programs among high-risk populations seem to be effective in reducing the impact of the COVID-19 pandemic on HCC management.
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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 | 0.009 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".