Symptom severity and complexity trends in patients undergoing radiation therapy
Bibliographic record
Abstract
Abstract Objective: Symptom severity has considerable impact on patients’ cancer care journey. This study aims to better understand psychological and physical symptom scores of radiotherapy patients across their radiotherapy care trajectory. Methods: 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. Within the study period, time points included consultation, first and last radiation treatment reviews and first post-treatment follow-up. Symptoms were divided into psychological and physical. Mixed effect models assessed trajectories of psychological and physical scores across appointments. A symptom complexity score was assigned to each ESAS-r encounter. Symptom complexity score association with appointment type and tumor group was modelled using Generalized Estimating Equations (GEE). Results: The study cohort consisted of 1,632 patients who completed 2,519 ESAS-r questionnaires. Patients reported significantly higher psychological symptom scores at consultations than at first review, last review and follow-up. Patients reported significantly higher physical scores at last reviews compared to consultations. Patients at first review had significantly lower odds of having a higher (more severe) symptom complexity score, compared with patients at consultations (OR =0.77, 95% CI=0.64-0.93). Conclusions: Symptoms change over the course of a patient’s care trajectory. Understanding how particular symptoms change over time provides a target for initiatives that improve symptom 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 | 0.006 |
| 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".