[Palliative Care in Nephrology: A National Survey].
Bibliographic record
Abstract
The demographic and epidemiological transition that Italy has undergone over the past 50 years has made palliative care (PC) essential in situations where active treatment is no longer proportionate. One of the major current and future challenges of palliative care lies in the management of non-oncological chronic diseases, particularly advanced organ failure and dementia. Individuals aged over 70 and 80 represent a significant proportion of the more than 45,000 chronic dialysis patients in Italy. These patients typically present with increased comorbidities, reduced life expectancy and quality of life, and a higher risk of hospitalization and in-hospital mortality. Moreover, older adults in these age groups also constitute the majority of the approximately 15% of patients with end-stage renal disease (ESRD) who continue exclusively with conservative management, never initiating renal replacement therapy. Over the past decade, interest in palliative care within the field of nephrology has grown substantially across the Western world. Decisions to withhold or withdraw dialysis vary widely by region, with higher rates observed in North America and Canada, and lower rates in Southern Europe. The Italian Society of Palliative Care (SICP) published with the Italian Society of Nephrology (SIN) and the Italian Society of Nephrology Nurses (SIAN), ten and six years ago respectively, a consensus document on the integration of palliative care in the management of chronic kidney disease. The objective of this study is to provide a comprehensive overview of the current state of palliative care practices within nephrology units across Italy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".