Trends in symptom severity and complexity in patients undergoing radiation therapy
Bibliographic record
Abstract
Symptom severity and complexity have considerable impact on a patient's cancer care journey. This study describes symptom scores of radiotherapy patients across their radiotherapy care trajectory and factors associated with symptom complexity. Patients who received radiotherapy at a single tertiary cancer center, who also completed at least one symptom-reporting questionnaire, the Edmonton Symptom Assessment Scale- Revised (ESAS-r) between October 1, 2019 and April 1, 2020 were included in this retrospective analysis. Symptom assessment time points were pre-treatment, start and end of radiation treatment and post-treatment follow-up. Mean ESAS-r scores for individual symptoms were descriptively analyzed by assessment timing and tumour group. We calculated a symptom complexity score for each ESAS-r measurement, using a validated algorithm, and assigned overall symptom complexity as low, moderate or severe. We modelled the association between assessment timing, and tumor group, with symptom complexity using Generalized Estimating Equations (GEE). The study cohort consisted of 1,632 patients who completed 2,519 ESAS-r questionnaires. Patients with lung and H&N cancers reported higher mean symptom scores compared to other tumour groups. Patients at the start of treatment had significantly lower odds of having a more severe symptom complexity, compared with patients pre-treatment (OR = 0.77, 95% CI = 0.64-0.93). Patients with H&N and lung cancer and patients prior to starting radiation may benefit most from increased symptom support and management.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".