An eHealth system supporting palliative care for patients with non–small cell lung cancer
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). METHODS: 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. RESULTS: 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. CONCLUSIONS: 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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".