Bibliographic record
Abstract
For almost a decade, 'early integration' has become a buzzword in the palliative care community. Can this still be an issue of controversy? The goals of care in palliative medicine are beyond any criticism and in fact should be, at least in theory, goals of good oncological care. However, the reality presents a different picture. The care of cancer patients requires improvement, and the studies on the early integration of palliative care (EIPC) reveal deficits in the oncological practice. However, the limitations and methodological weaknesses of these EIPC studies are insufficiently analyzed and discussed. The main criticisms relate to the incomplete definition of primary endpoints, published analyses deviating from the study protocols and insufficient consideration for multiple testing. If this criticism is justified, a possible consequence would be to overrate the achievable effects of EIPC and to limit the use of these studies in guiding policies. Improving the care of cancer patients by fostering their primary care by oncologists could provide one of the alternative approaches, but needs to be evaluated in future studies. Unmet needs in physical, psychic, spiritual or social care need to be addressed. Whether this requires a multiprofessional team in all cases is another issue of discussion.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".