Systematic self-reporting of patients’ symptoms: improving oncologic care and patients’ satisfaction
Bibliographic record
Abstract
Abstract Background: In recent years, there has been a growing interest to enhance patients’ symptom management during routine cancer care using patient-reported outcome measures. The goal of this study is to analyse patients’ responses to the Edmonton Symptom Assessment System (ESAS) to determine whether patient-reported outcomes could help characterise those patients with the highest supportive care needs and symptom burden in order to help provide targeted support for patients. Methods: In this study, we analysed ESAS questionnaire responses completed by patients as part of their routine care and considered part of patients’ standard of care. Statistical analyses were performed using the IBM SPSS Statistics version 26.0. Descriptive statistics are used to summarise patient demographics, disease characteristics and patient-reported symptom severity and prevalence. Results: The overall mean age is 65.2 ± 12.8 years comprising 43.8% male and 56.2% female patients. The five common primary disease sites are breast (26.2%), haematology (21.1%), gastrointestinal (15.3%), genitourinary (12.7%) and lung (12.0%) cancers. The mean severity for each symptom is all mild (score: 1–3). The three most common reported symptoms causing distress are tiredness, poor overall wellbeing and anxiety, and the least reported symptom is nausea. Conclusions: Systematic self-reporting of patients’ symptoms is important to improve symptom management, timely facilitation of appropriate intervention, patient experience, and patient and family satisfaction. The awareness of disease site, gender and age-related symptom variations should help in the design and provision of appropriate symptom-directed, tumour-specific and patient-focused interventions to meet patients’ immediate 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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".