JOURNAL WATCH A Single Set of Numerical Cutpoints to Define Moderate and Severe Symptoms for the Edmonton Symptom Assessment System.
Bibliographic record
Abstract
Symptom intensity in cancer and palliative care patients is frequently assessed using a 010 ranking score. Results are then often grouped into verbal categories (mild, moderate, or severe) to guide therapy. Numerical cutpoints separating these categories are often variable, with previous work suggesting different cutpoints across different symptoms, which is unwieldy for clinical use.. The Edmonton Symptom Assessment Symptom (ESAS) assesses nine common symptoms using this 010 scale. The primary aim of this study was to examine the relationship between the numerical and verbal scores using the ESAS and to identify a single cutpoint to separate severe and nonsevere symptomatology. A second goal was to similarly identify a cutpoint to separate moderate or severe from none or mild symptom intensity. Consenting patients (n=400) completed both a standard ESAS and an identical form that replaced 010 with none, mild, moderate, and severe. Receiving operating characteristics curves were generated to identify the best fit between sensitivity and specificity. For the 'severe' ranking, six symptoms had a best fit of 7, with sensitivity for the remaining three symptoms still greater than 80%. For the combined grouping of moderate or severe, results were less uniform. A cutpoint of either 4 or 5 would be supported by our data, with a greater sensitivity using 4 and improved specificity using 5 as the cutpoint. Across all ESAS symptoms, then, 7 or higher represents a severe symptom by patient definition, whereas a cutpoint of either 4 or 5 could reasonably define combined moderate and severe symptoms. Strengths A prospective study Adequate sample of palliative care patients. Stats well done with good sensitivity and specificity levels. Weaknesses Patients are highly functional (median PPS of 70% This could also explain the low number of patients who scored as severe symptoms: nausea (10), depression (23), anxiety (28) and shortness of breath(14). Therefore it is unclear as how the endoflife distress might have interfered with patients' definitions of their symptoms (mild, moderate, severe). Relevance to palliative care The ESAS is a commonly used tool to better assess patients and to facilitate communication with the different members of the team involved in the care. This study will help to better understand the meaning of the scores. It also opens a door to further research dedicated specifically to more advanced cancer patients and their perceptions of the 9 symptoms included in the ESAS.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.097 | 0.043 |
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".