Symptom Monitoring in Glioma Patients: Development of the Edmonton Symptom Assessment System Glioma Module
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Symptoms in glioma patients are distinctly different from symptoms in patients with other types of cancer and have a high impact on quality of life. In this study, a stepwise approach of developing a glioma module for assessment of symptoms, based on a Dutch adapted and validated version of the Edmonton Symptom Assessment System, is described. METHODS: Three phases of instrument development were conducted: a systematic literature review and a focus group interview with experts were performed (phase I) to generate relevant symptoms and construct a preliminary module (phase II). In phase III, the preliminary module was evaluated (n = 25) and pretested (n = 45) in glioma patients representing all phases of the disease. RESULTS: Our glioma module contains 11 generic and 6 neurologic symptoms. Patients completed the glioma module in a median of 5 minutes, and 56% of the patients required some assistance to complete the instrument. CONCLUSION: The glioma module has initial validity and will benefit from prospective validation in a larger cohort of patients with glioma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.010 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".