[Approaching patients who require palliative care].
Bibliographic record
Abstract
The aim of palliative medicine is the best possible quality of life. Based upon literature and clinical experience we address factors of importance for the meeting between the palliative patient and the physician. Family and network, personality and behaviour vary between palliative patients and have effect upon their coping; these factors should be reflected in the meeting between the physician and the patient. Communication with a palliative patient also aims at a systematic assessment of his or her various symptoms and the physician should have a broad armamentarium of communications skills. The optimal way to assess symptoms is by use of clinical interviews supplemented with standardized measurements. Instruments such as the Edmonton Symptom Assessment System are very useful in the assessment of the commonest symptoms. The assessments should include patients' perspectives and what priority they give to relevant interventions. Treatment should be evaluated systematically in order to avoid ineffective treatments and to reduce side effects and interactions. The aim is to give patients as good and as long a time as possible in the place in which they want to spend the last part of their lives. Palliative medicine often combines the art of medicine with new technology. The focus on quality of life and the patient perspective is paramount, and the approach to the patient should reflect this.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.047 | 0.026 |
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".