Minimal clinically important differences in the Edmonton Symptom Assessment Scale in cancer patients: A prospective, multicenter study
Bibliographic record
Abstract
BACKGROUND: The Edmonton Symptom Assessment Scale (ESAS) is widely used for symptom assessment in clinical and research settings. A sensitivity-specificity approach was used to identify the minimal clinically important difference (MCID) for improvement and deterioration for each of the 10 ESAS symptoms. METHODS: This multicenter, prospective, longitudinal study enrolled patients with advanced cancer. ESAS was measured at the first clinic visit and at a second visit 3 weeks later. For each symptom, the Patient's Global Impression ("better," "about the same," or "worse") was assessed at the second visit as the external criterion, and the MCID was determined on the basis of the optimal cutoff in the receiver operating characteristic (ROC) curve. A sensitivity analysis was conducted through the estimation of MCIDs with other approaches. RESULTS: For the 796 participants, the median duration between the 2 study visits was 21 days (interquartile range, 18-28 days). The area under the ROC curve varied from 0.70 to 0.87, and this suggested good responsiveness. For all 10 symptoms, the optimal cutoff was ≥1 point for improvement and ≤-1 point for deterioration, with sensitivities of 59% to 85% and specificities of 69% to 85%. With other approaches, the MCIDs varied from 0.8 to 2.2 for improvement and from -0.8 to -2.3 for deterioration in the within-patient analysis, from 1.2 to 1.6 with the one-half standard deviation approach, and from 1.3 to 1.7 with the standard error of measurement approach. CONCLUSIONS: ESAS was responsive to change. The optimal cutoffs were ≥1 point for improvement and ≤-1 point for deterioration for each of the 10 symptoms. Our findings have implications for sample size calculations and response determination.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".