The Optimal Cutoff Point for Expressing Revised Edmonton Symptom Assessment System Scores as Binary Data Indicating the Presence or Absence of Symptoms
Bibliographic record
Abstract
CONTEXT: Terminally ill patients with cancer experience various physical and emotional symptoms that have a negative impact on quality of life and activities of daily living. Recently, revised Edmonton Symptom Assessment System (ESAS-r) scores have been proposed for assessing symptoms in terminally ill patients with cancer. OBJECTIVE: To determine the optimal cutoff point for expressing ESAS-r scores as binary data, indicating the presence or absence of symptoms. METHODS: We conducted a retrospective study of patients hospitalized in the palliative care unit of our hospital between September 1, 2014 and May 31, 2015. To determine the optimal cutoff point for expressing ESAS-r scores as binary data, indicating the presence or absence of 6 physical symptoms ("pain," "tiredness," "drowsiness," "nausea," "lack of appetite," and "dyspnea"), the sensitivity and specificity of each measurement were calculated. Cutoff points were estimated using receiver operating characteristic curve analysis. RESULTS: Data from 157 patients who performed the self-assessment in ESAS-r scores were analyzed. The mean age was 66.5 years. Approximately 60.0% of patients were male. The optimal cutoff point for pain, tiredness, drowsiness, nausea, lack of appetite, and dyspnea was 4, 4, 4, 2, 5, and 4, respectively. The area under the curve for tiredness, nausea, and dyspnea was >0.70, followed in order by pain, lack of appetite, and drowsiness. The area under the curve for drowsiness was 0.55. CONCLUSION: Our results suggest that physical symptoms other than drowsiness could potentially predict ESAS-r score severity.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".