Experience of Newly Diagnosed Patients With Sarcoma Receiving Chemotherapy
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To examine symptom distress and quality of life (QOL) in newly diagnosed patients with sarcoma receiving chemotherapy. DESIGN: Pilot study; descriptive, quantitative. SETTING: Urban community cancer center in the northeastern United States. SAMPLE: 11 newly diagnosed patients with sarcoma. METHODS: Participants completed the Edmonton Symptom Assessment Scale and the Functional Assessment of Cancer Therapy-General at baseline and on days 1, 15, and 21 of their chemotherapy treatment. MAIN RESEARCH VARIABLES: Symptom distress and QOL. FINDINGS: Fatigue was the most prevalent and pervasive symptom. Anxiety, well-being, lack of appetite, drowsiness, and depression were the most commonly reported symptoms during chemotherapy. QOL was negatively affected. The lowest mean score reported was for functional well-being. Outcome profiles for symptom distress increased over time, whereas QOL profiles decreased over time. Exploratory analyses of age, race, sex, and diagnosis group suggested differences that warrant further study. CONCLUSIONS: Overall, increasing symptom distress and reduced QOL over time were reported by patients with sarcoma during chemotherapy. Exploratory analysis by demographic variables and treatment group suggested the need for further research of predictors for symptom distress and QOL. IMPLICATIONS FOR NURSING: Clinical and research implications included the need for better understanding about symptom distress and QOL predictors in patients with sarcoma, as well as the evaluation of interventions directed to address this population's specific needs.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".