Caregivers' accuracy in reporting patients' symptom: A preliminary report.
Bibliographic record
Abstract
64 Background: Improving symptom control during end of life (EOL) is a core goal of palliative care. When patients are not able to report their symptoms, caregivers (CG) report symptom intensity as surrogates. Data show that there is good agreement between patients and CG in reporting symptom intensity. However, little is known about factores that influence CGs’ accuracy. The aim of the study was to determine CG accuracy of advanced cancer patients’ symptoms and to identify CG factors that could modify it. Methods: In this prospective study, patients with advanced cancer enrolled in the National Program of Palliative Care at a public Hospital in Santiago, Chile and their CGs independently scored ten patients’ symptoms using the Edmonton Symptom Assessment Scale (ESAS). Correlation between patient and CG scores were estimated for each symptom. Differences between patient and CG scores were calculated for each symptom and were transformed into positive values. A continuous variable was created with the sum of all the differences as an indicator of caregiver overall accuracy, with smaller scores meaning better accuracy. CG depression, anxiety (HADS) and burden (ZARIT) were also assessed. Results: 36 patients and their CG were included in this preliminary analysis. Mean patient age was 64, 20 (56%) females, 13 (36%) had GI cancer, 7 (19%) lung cancer and 16 (45%) other. Mean caregiver age was 53, 25 (69%) female. We found positive correlations between patients’ and caregivers’ assessment of pain, fatigue, nausea, anorexia, dyspnea, depression and insomnia (r > 0.3 and p < .05 for each symptom). CG accuracy ranged between 10 and 44 points (mean 25, standard deviation 9) and was not associated with CG age, gender, depression, anxiety or burden. CG accuracy was negatively associated with CG worrying thoughts as assessed by one of the HADS questions (Coef -3.99, p = .015), meaning that CG were more accurate when their worrying thoughts were higher. This association remained significant when adjusted by CG depression, anxiety and burden. Conclusions: CG are more accurate in reporting patient symptoms when their levels of worrying thoughts are higher. This information may have implications in interpreting CG report during EOL.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Methods · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".