Symptom burden and operational metrics in gynecological cancer patients cared by a palliative care team, in Greece
Bibliographic record
Abstract
INTRODUCTION: The integration of palliative care into standard gynecologic oncology care, in developed countries, is considered to be associated with cost-savings, longer survival, lower symptom burden, and better quality of life for patients and caregivers. u201cGALILEEu201d is the first comprehensive Palliative Care Unit in Greece. The aim of the study is to characterize symptom prevalence and identify operational metrics in patients with gynecologic cancer, cared at home by an interdisciplinary palliative care teamMETHODS: Retrospective analysis of all women, with gynecologic cancers, cared from 01.03.2015- 31.12.2018, by u201cGALILEEu201d. Electronic patient records were reviewed for demographics, disease characteristics and end of life discussions. Performance status was measured by the Palliative Performance Scale (PPS) and symptom burden by the Edmonton Symptom Assessment System (ESAS).RESULTS: Out of 520 patients cared in the same time period, only 42 (8)%) were identified with gynecologic cancers. Their median age was 68,5 years (range, 44-94). The majority had ovarian cancer (47,6%). Half of the patients were admitted during first line treatment. Only two patients (4,7%) were referred by their gynecologic oncologist. Median time in care was 97,5 (5-2151) days.At study entry, most patients (61%) had a good performance status (PPS>50%), while the most common disease-related symptoms were pain (78,6%), fatigue (54,8%), nausea (52,4%), anxiety (45,2%) and depression (31%). 43,9% of the patients had no emergency admission to the hospital , 42,8% of patients died of their disease and 50% were supported to die at home. In the last 2 weeks of life 7,1% of patients were receiving chemotherapy and 31% had a hospital admission.Upon admission, only 23,8% of patients were aware of the diagnosis and 11,9% of the prognosis.CONCLUSION: Gynecologic oncology outpatients have a high symptom burden and information needs and could thus profit by concurrent provision of palliative care, early on their disease trajectory.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".