Symptoms, Quality of Life, and Daily Activities in People With Newly Diagnosed Solid Tumors Presenting to a Medical Oncologist
Bibliographic record
Abstract
INTRODUCTION: Symptom and Quality of Life (QOL) data are important patient reported outcomes. Early identification of these is critical for appropriate interventions. Data collection may be helped by modern information technology. AIM: This study examined symptoms and QOL in people with solid tumors at their first visit to a medical oncologist. We also evaluated the clinical utility of tablet computers (TC) to collect this data. METHODS: This was a prospective study of 105 consecutive patients in the cancer outpatient clinic of a tertiary level academic medical center. Symptom and QOL data was collected by TC with wireless database upload. RESULTS: One-third participants had moderate to severe pain; almost half clinically significant pain that interfered with daily activities. Tiredness, anxiety, and drowsiness were common (prevalence - 79%, 63% and 50% respectively). One-third of those who had items identified from the Edmonton System Assessment System also volunteered other symptoms, mostly gastrointestinal problems. Many of those affected also reported impaired Global Wellbeing and low Overall QOL. There was a 98% completion rate, which took on average ten minutes. Direct observation and informal feedback from patients and physicians regarding the acceptability of TC in this setting was uniformly positive. CONCLUSIONS: Amongst people with newly diagnosed solid tumors clinically important psychological and physical symptoms, QOL problems and difficulties with daily activities were commonly present in the 24-hour period and in the week before a first Medical Oncology visit. Symptom and QOL data collection by TC in busy outpatient clinics showed good clinical utility.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".