Palliative care in gynecologic oncology: a narrative review of current literature and vision for the future
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Several professional societies have recommended incorporating palliative care into routine oncology care, yet palliative care remains underutilized among women with gynecologic cancers. This narrative review highlights current evidence regarding utilization of palliative care in gynecologic oncology care. Additionally, the authors offer recommendations to increase early integration and utilization of palliative care services, improve education for current and future gynecologic oncology providers, and expand the palliative care workforce. METHODS: The authors reviewed studies of palliative care interventions in oncology settings, with an emphasis on studies that included women with gynecologic malignancies. A panel of author/experts were gathered for a semi-structured interview to discuss the future of palliative care in gynecologic cancer care. The interview was recorded and reviewed to highlight themes. KEY CONTENT AND FINDINGS: Data supports routine integration of palliative care into gynecologic oncology practice. To expand delivery of palliative care, additional research that investigates implementation of palliative care across different healthcare settings is needed. There is a shortage of palliative care providers in the United States. Therefore, it is critical for gynecologic oncologists to receive a robust education in primary palliative care skillsets. Additionally, to expand the specialty palliative care workforce, palliative medicine leaders should recruit more gynecologic oncologists and other surgeons into palliative care fellowship programs. CONCLUSIONS: Expanded utilization of palliative care offers an opportunity to improve quality of care and outcomes for women with gynecologic cancers.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".