SMART-ESAS: Smartphone Monitoring and Assessment in Real Time of Edmonton Symptom Assessment System Scores for Patients With Cancer
Bibliographic record
Abstract
PURPOSE Serial patient-reported outcome (PRO) measurements in clinical practice are associated with a better quality of life and survival. Recording electronic PROs using smartphones is an efficient way to implement this. We aimed to assess the feasibility of the electronically filled Edmonton Symptom Assessment System (e-ESAS) scale in the lower-middle–income country (LMIC) setting. METHODS Baseline clinical features and conventional paper-based ESAS (p-ESAS) were collected in newly diagnosed patients with solid organ tumors. Text message link was sent to these patients for filling e-ESAS. ESAS was categorized into physical, psychological, and total symptom domains. Scores were divided into none to mild (0-3) and moderate to severe (4-10). Intraclass correlation coefficients (ICCs) were used to determine the correlation between p-ESAS and e-ESAS. Multivariable logistic regression was used to identify independent factors affecting symptom burden. RESULTS Of 1,160 participants who filled out p-ESAS, 595 completed both e-ESAS and p-ESAS questionnaires and were included in the final analysis. Moderate to severe physical, psychological, and total symptom scores were seen in 39.8%, 40%, and 39% of participants. Tiredness and anxiety were the most common physical and psychological symptoms, respectively. ICCs between the p-ESAS and e-ESAS varied between 0.75 and 0.9. Total symptom scores were independently predicted by metastatic disease (odds ratio [OR], 1.83; 95% CI, 1.26 to 2.67; P = .001) and a higher level of education (OR, 0.42; 95% CI, 0.25 to 0.72; P = .001). CONCLUSION Paper-based and electronically filled ESASs have good intraobserver reliability across individual symptoms and domain scores in a representative cohort at a tertiary care institute in the LMIC. This may help us incorporate e-ESAS in routine clinical care in the real-world setting with financial, infrastructural, and manpower limitations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".