Intérêts de l’intégration précoce de soins palliatifs aux soins oncologiques : une revue rapide des écrits
Bibliographic record
Abstract
With the number of cancer diagnoses and cancer-related deaths on the rise, palliative care is becoming a more important consideration for helping to improve the quality of life of patients and families and the support they receive during their healthcare journey. Accordingly, the early integration of palliative care into standard oncology care would appear to be an underutilized and novel approach that could be used to address the specific needs of palliative oncology patients. Oncology nurses play a central role in this process, delivering care throughout the health continuum, including palliative care. The purpose of this rapid review is to outline the benefits of early palliative care interventions and describe their characteristics. A literature search on CINAHL and PubMed returned five randomized trials conducted between 2010 and 2018. An analysis of these papers showed that the majority of the selected studies concluded that the early integration of palliative care into standard oncology care, which includes such treatments as chemotherapy and radiation therapy, can lead to improvements in quality of life, symptoms of anxiety and depression, and overall survival rate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".