Quality-of-Life Issues in Patients With Ovarian Cancer and Their Caregivers:
Bibliographic record
Abstract
UNLABELLED: Significant progress has been made towards the treatment of ovarian cancer resulting in longer median survival despite a persistent low cure rate. Relatively few studies have examined the impact of the cancer and its treatment on the patients and their caregivers due to the difficulty in the definition and measurement of the Quality of Life (QOL) concept. A review of the literature revealed significant alterations in the quality of life of ovarian cancer patients during treatment and long term follow ups. For the caregivers, it is important for health care providers to realize that: 1) caregivers are being asked to assume an increasing number of complex care giving tasks at home, 2) there exists a high proportion of unmet caregiver needs, 3) the care giving experience includes both positive and negative elements and, 4) perception of caregivers' burden is positively linked to negative reactions to care giving. Supportive programs for patients and caregivers should be designed with these needs in mind. Future research should study the best way to incorporate results of quality of life assessments into routine treatment decision-making. TARGET AUDIENCE: Obstetricians & Gynecologists, Family Physicians. LEARNING OBJECTIVES: After completion of this article, the reader should be able to outline the current data on QOL issues in patients with ovarian cancer, and to describe potential working definitions of QOL.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".