Patients With Cancer and Next-of-Kin Response Comparability on Physical and Psychological Symptom Well-being
Bibliographic record
Abstract
Next of kin (NOK) play an integral role in fostering optimal quality of life in symptomatic patients who are coping with cancer in the home setting. Often when patients in advanced stages of cancer are no longer able to meaningfully communicate their illness and symptom needs, healthcare professionals turn to NOK to provide sound estimates of patients' symptom experiences. This overview is based on 37 research studies written between 1987 and 2002 and updates an earlier overview of 13 studies on patient-NOK response comparability. The purpose is to, first, promote a better comprehension of methodologies and statistical techniques commonly employed to measure patterns of response comparability (or levels of agreement) between patient self-reports and NOK estimates on patient quality-of-life experiences of physical or symptom and emotional or psychological well-being. The second aim is to identify conditions where NOK may pose as reasonably accurate judges of patients' health-related quality of life, particularly symptom experiences arising from various diagnoses, including cancer. Third, subsequent to identifying the gaps in current research knowledge and limitations in study designs, recommendations for statistical and methodological techniques are outlined.
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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 | 0.028 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".