Errors in Symptom Intensity Self-Assessment by Patients Receiving Outpatient Palliative Care
Bibliographic record
Abstract
BACKGROUND: Patient-based symptom scores are the standard method for assessment in palliative care. There has been limited research on the frequency of errors upon using this approach. The Edmonton Symptom Assessment Scale (ESAS) is a reliable and valid assessment tool routinely used for symptom intensity assessment in our cancer center. OBJECTIVE: To determine if patients were scoring the symptoms on the ESAS in the way it was supposed to be scored. SETTINGS: The study was carried out at the outpatient palliative care center. DESIGN AND SUBJECTS: Retrospective review of 60 consecutive patient charts was done where the patient had initially scored the ESAS. The physician looked at this scoring on the ESAS and went back to the patient to do the scoring again to see if the patient had scored it in the way it was intended to be scored. The same physician did the assessment on all of the patients. OUTCOME MEASURES: Level of agreement (weighted kappa) before versus after the physician visit; Screening performance of patient completed ESAS for mild and moderate symptom intensity. RESULTS: Complete agreement ranged from 58% (sleep) to 82% (well-being); the weighted kappa ranged from 0.49 (drowsiness) to 0.78 (well-being). There was more agreement for symptoms such as dyspnea, nausea, anxiety, and depression and less agreement for symptoms such as lack of sleep and lack of appetite. The screening performance of the initial patient self assessment showed less sensitivity for nausea, drowsiness if the intensity was mild and less sensitivity for pain, nausea, anxiety, and drowsiness if the intensity was moderate. CONCLUSIONS: Vigilance needs to be maintained about the ESAS scores done by the patients particularly for symptoms of sleep, appetite, and pain. There is a likelihood of error if doctors or nurses do not routinely check the way patients have completed the assessment form. More research is needed to determine the best way to teach patients how to minimize errors in self-reporting of symptoms.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".