Identification of palliative care needs and prognostic factors of survival in tailoring appropriate interventions in advanced oncological, renal and pulmonary diseases: a prospective observational protocol
Bibliographic record
Abstract
INTRODUCTION: It is estimated that of those who die in high-income countries, 69%-82% would benefit from palliative care with a high prevalence of advanced chronic conditions and limited life prognosis. A positive response to these challenges would consist of integrating the palliative approach into all healthcare settings, for patients with all types of advanced medical conditions, although poor clinician awareness and the difficulty of applying criteria to identify patients in need still pose significant barriers. The aim of this project is to investigate whether the combined use of the NECPAL CCOMS-ICO and Palliative Prognostic (PaP) Score tools offers valuable screening methods to identify patients suffering from advanced chronic disease with limited life prognosis and likely to need palliative care, such as cancer, chronic renal or chronic respiratory failure. METHODS AND ANALYSIS: This multicentre prospective observational study includes three patient populations: 100 patients with cancer, 50 patients with chronic renal failure and 50 patients with chronic pulmonary failure. All patients will be treated and monitored according to local clinical practice, with no additional procedures/patient visits compared with routine clinical practice. The following data will be collected for each patient: demographic variables, NECPAL CCOMS-ICO questionnaire, PaP Score evaluation, Palliative Performance Scale, Edmonton Symptom Assessment System, Eastern Cooperative Oncology Group Performance Status and data concerning the underlying disease, in order to verify the correlation of the two tools (PaP and NECPAL CCOMS-ICO) with patient status and statistical analysis. ETHICS AND DISSEMINATION: The study was approved by local ethics committees and written informed consent was obtained from the patient. Findings will be disseminated through typical academic routes including poster/paper presentations at national and international conferences and academic institutes, and through publication in peer-reviewed journals.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".