Caregiver symptom burden assessment using the Edmonton Symptom Assessment System (ESAS): A preliminary report.
Bibliographic record
Abstract
227 Background: Regular assessment of caregiver symptom burden during patient visits would allow prompt referral for their care. The ESAS is a multidimensional tool used in patients but not caregivers. The objectives of this study were to determine the feasibility of the ESAS in assessing caregiver symptoms defined as completing 9/12 items, assess caregiver-reported usefulness of its completion, determine the association of symptom scores between patients, caregivers and various clinical and psychosocial factors, and determine concurrent validity with the Zarit Burden Interview-12 (ZBI-12). Methods: A prospective study of 90 patient-primary caregiver dyads in an outpatient Supportive Care Center in a cancer center was conducted. The 12 item ESAS-FS was completed by the dyads with other measures of clinical and psychosocial factors [demographics, cancer diagnosis, co-morbidities, caregiving activities, prognostic index and patient’s performance status]. Results: The ESAS is a feasible tool to assess caregiver symptom burden with 90/90 caregivers [100%] completing at least 9/12 items; 66/90 caregivers [73%] found ESAS useful to report their symptom burden. A significant association was found between ESAS scores of caregivers and patients in depression [p < 0.01], psychosocial items [depression, anxiety, well-being, financial distress, spiritual pain; p < 0.01], and total symptom distress scores [p < 0.01]. Caregiver employment status [p = 0.03] and total caregiver activities [p = 0.04] were significantly associated with total caregiver ESAS scores. There was no significant association between patient and caregiver co-morbidities [p = 0.08], prognostic index [0.07] and performance status [p = 0.26] to total caregiver ESAS scores. Caregivers recommended certain physical symptoms such as pain and nausea may be eliminated. Concurrent validity with ZBI-12 was not achieved [r = 0.53, p = 0.74] suggesting that ESAS measured different caregiver dimensions. Conclusions: The ESAS is a feasible tool to measure caregiver symptoms and was found useful by caregivers. Further research is needed to modify the ESAS based on caregiver’s recommendations and further psychometric studies need to be conducted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".