Effect of COVID-19 Pandemic on Hepatocellular Carcinoma Diagnosis: Results from a Tertiary Care Center in North-West Italy
Bibliographic record
Abstract
The COVID-19 pandemic has forced us to direct most of the available resources towards its management. This has led to the neglect of all other pathologies, including cancer. The aim of this study was to verify whether the difficulty in accessing the health system has led to a reduction in new diagnoses of hepatocellular carcinoma (HCC) and whether this has already been reflected in a more advanced stage of the cancer. A single-center, retrospective study including adult patients with a new diagnosis of HCC was performed. Patients were divided into three groups: the prelockdown phase (May 2019–February 2020), the lockdown phase (March 2020–December 2020), and the postlockdown phase (January 2021–October 2021); 247 patients were included. The number of patients diagnosed with HCC distinctly diminished in the periods March 2020–December 2020 (n = 69; −35%) and January 2021–October 2021 (n = 72; −32%) as compared to the period May 2019–February 2020 (n = 106). Noteworthy was the reduced surveillance in the period January 2021–October 2021 as compared to May 2019–February 2020 (22.9% vs. 36.6%, p = 0.056). No significant changes have yet been observed in tumor characteristics (BCLC staging distribution remained unvaried, p = 0.665). In conclusion, the number of new HCC diagnoses decreased sharply in the first 2 years of the pandemic, with no worsening of the stage. A more advanced stage of the disease could be expected in the next few years in patients who have escaped diagnosis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".