Lessons Learned in the Assessment of Health-Related Quality of Life: Selected Examples From the National Cancer Institute of Canada Clinical Trials Group
Bibliographic record
Abstract
In this article, we provide a brief historical review of the development of patient-reported outcome measurement, analysis, and reporting in clinical trials of the National Cancer Institute of Canada Clinical Trials Group (NCIC CTG). In doing so, we examine selected lessons learned in furthering the quality of these data and their application to clinical practice. We conclude that sequential institution of key policies within the NCIC CTG and the development of a collective philosophy within the group has enabled the routine incorporation of health-related quality of life into clinical trial protocols according to robust scientific principles; that collection of quality data is possible in a variety of circumstances (although not universally so); that patient-reported data on subjective experiences is likely to be more reliable and valid than conventional toxicity information; and that simple analyses that report group trends as well as individual patient response rates are preferred.
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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 | 0.066 | 0.099 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".