Bibliographic record
Abstract
INTRODUCTION: Many patients and their families are hesitant to consult a palliative care (PC) team. In 2014, approximately 6,000,000 people in the United States could benefit from PC, and this number is expected to increase over the next 25 years. OBJECTIVES: The purpose of this review is to shed light on the significance of PC and provide a holistic view outlining both the benefits and existing barriers. METHODS: A literature search was conducted using MEDLINE (PubMed), Cochrane Central Register of Controlled Trials, and Web of Science to identify articles published in journals from 1948 to 2019. A narrative approach was used to search the grey literature. DISCUSSION: Traditionally, the philosophy behind PC was based on alleviating suffering associated with terminal illnesses; PC was recommended only after other treatment options had been exhausted. However, the tenets of PC are applicable to anyone with a life-threatening illness as it is beneficial in conjunction with traditional treatments. It is now recognized that PC services are valuable when initiated alongside disease-modifying therapy early in the disease course. Studies have shown that PC decreased total symptom burden, reduced hospitalizations, and enabled patients to remain safely at home. CONCLUSION: As the population ages and chronic illnesses become more widespread, there continues to be a growing need for PC programs. The importance of PC should not be overlooked despite existing barriers such as the lack of professional training and the cost of implementation. Education and open discussion play essential roles in the successful early integration of PC.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".