Overall Quality of Life Assessment in the Patients Undergoing External Beam Radiation in Outpatient
Bibliographic record
Abstract
Background: The impact of treatment on cancer patients ’ quality of life (QoL) has been the focus of a variety of longitudinal studies in English literature for past decade. The measurement of patient-reported outcomes which includes health-related quality of life is a new important initiative which has emerged and grown over the past three decades. Following the development of reliable and valid self-reported questionnaires, health-related quality of life has been assessed in tens of thousands of patients and a wide variety of cancers. With growing information, feedback and experience, the quality of the health-related QOL studies has improved a lot. We expect in near future more methodologically robust studies will be done in a scientific way to answer unanswered questions. Methods: As part of a Dean's summer project, a survey was undertaken to facilitate a more complete description of the quality of life experience in patients with histological diagnosis of cancer undergoing external beam radiation as an outpatient at Allan Blair Cancer Center, Regina, Canada. The questionnaires had two major components: depression and global QOL. The depression was measured by the Zung Self-Rating Depression Scale which is a short self-administered survey to quantify the depression status of a patient. Results: Overall, only the equation associated with the outcome of QoL- Physical well-being was significant.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".