An eHealth System Supporting Palliative Care for Patients with Non-Small Cell Lung Cancer: A Randomized Trial
Bibliographic record
Abstract
BACKGROUND: In this study, the authors examined the effectiveness of an online support system (Comprehensive Health Enhancement Support System [CHESS]) versus the Internet in relieving physical symptom distress in patients with non-small cell lung cancer (NSCLC).\nMETHODS: In total, 285 informal caregiver-patient dyads were assigned randomly to receive, for up to 25 months, standard care plus training on and access to either use of the Internet and a list of Internet sites about lung cancer (the Internet arm) or CHESS (the CHESS arm). Caregivers agreed to use CHESS or the Internet and to complete bimonthly surveys; for patients, these tasks were optional. The primary endpoint-patient symptom distress-was measured by caregiver reports using a modified Edmonton Symptom Assessment Scale.\nRESULTS: Caregivers in the CHESS arm consistently reported lower patient physical symptom distress than caregivers in the Internet arm. Significant differences were observed at 4 months (P = .031; Cohen d = .42) and at 6 months (P = .004; d = .61). Similar but marginally significant effects were observed at 2 months (P = .051; d = .39) and at 8 months (P = .061; d = .43). Exploratory analyses indicated that survival curves did not differ significantly between the arms (log-rank P = .172), although a survival difference in an exploratory subgroup analysis suggested an avenue for further study.\nCONCLUSIONS: The current results indicated that an online support system may reduce patient symptom distress. The effect on survival bears further investigation.
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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) | 0.001 | 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, 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".