Knowledge of Palliative Care in Men and Women Diagnosed With Metastatic Breast Cancer
Bibliographic record
Abstract
Purpose The purpose of this study was to evaluate knowledge of Palliative Care (PC) and the impact of systemic and patient-related factors on the use of PC in a diverse population of men and women diagnosed with metastatic breast cancer. Methodology A telephone administered survey was used with patients receiving treatment at a Cancer Center in an urban area of the Northeast US. Descriptive statistics and chi square analysis were used. Findings Of the 101 participants, 44% had no knowledge of PC and only 21.78% indicated that they were receiving palliative care. Participants who reported being followed by palliative care were less likely to have been treated in the emergency department in the past year ( P = 0.003) or to have been hospitalized ( P = 0.042). However, when asked about symptom burden, using the Edmonton Symptom Assessment Scale, patients who reported being followed by PC were more likely to report severe pain as compared to patients not receiving PC ( P < 0.001). There were no associations found between race/ethnicity or social determinants of health and knowledge of PC or receipt of services. Conclusions This sample of men and women diagnosed with metastatic breast cancer and being treated in a Cancer Center had limited knowledge and exposure to Palliative Care services across race and ethnicity. While no specific disparity was noted, the utilization of PC was low. Whether a function of a lack of referrals or patient preference, an effort should be made to increase PC referrals for all patients diagnosed with cancer.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".