A Descriptive Study of Repeated Hospitalizations and Survival of Patients with Metastatic Melanoma in the Northern Italian Region during 2004–2019
Bibliographic record
Abstract
BACKGROUND: Survival rates for metastatic melanoma (MM) patients have improved in recent years, leading to major expenses and health resource use. We conducted a non-concurrent prospective study to describe the burden of hospitalization in a real-world setting for patients with MM. METHODS: Patients were tracked throughout all hospital stays in 2004-2019 by means of hospital discharges. The number of hospitalizations, the rehospitalization rate, the average time spent in the hospital and the time span between consecutive admissions were evaluated. Relative survival was also calculated. RESULTS: Overall, 1570 patients were identified at the first stay (56.5% in 2004-2011 and 43.7% in 2012-2019). A total of 8583 admissions were retrieved. The overall rehospitalization rate was 1.78 per patient/year (95%CI = 1.68-1.89); it increased significantly with the period of first stay (1.51, 95%CI = 1.40-1.64 in 2004-2011 and 2.11, 95%CI = 1.94-2.29 thereafter). The median time span between hospitalizations was lower for patients hospitalized after 2011 (16 vs. 26 months). An improvement in survival for males was highlighted. CONCLUSIONS: The hospitalization rate of patients with MM was higher in the last years of the study. Compared with a shorter length of stay, patients were admitted to hospitals with a higher frequency. Knowledge of the burden of MM is essential for planning the allocation of healthcare resources.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".