Development of a Core Set of Patient-Reported Outcomes for Population-Based Cancer Survivorship Research: Protocol for an Australian Consensus Study
Bibliographic record
Abstract
BACKGROUND: Core outcome sets seek to improve the consistency and quality of research by providing agreed-upon recommendations regarding what outcomes should be measured as a minimum for a population and setting. The problems arising from a lack of outcome standardization in population-based cancer survivorship research indicate the need for agreement on a core set of patient-reported outcomes (PROs) to enhance data quality, consistency, and comparability. OBJECTIVE: This study aims to identify a core set of PROs, representing the most important issues impacting on cancer survivors' long-term health, functioning and quality of life, to inform population-based research on cancer survivorship. METHODS: In Phase I, a list of all potentially important outcomes will be generated through focus group discussions with cancer survivors and a review of measures for assessing quality of life in cancer survivorship. The consolidated list will be advanced to Phase II, where a stakeholder consensus process will be conducted with national experts in cancer survivorship to refine and prioritize the outcomes into a core outcome set. The process will consist of a two-round Delphi survey and a consensus meeting. Cancer survivors, oncology health care professionals, and potential end users of the core outcome set with expertise in cancer survivorship research or policy will be invited to participate. In Phase III, recommended measures for assessment of the core outcome set will be selected with advice from experts on the assessment, analysis, and interpretation of PROs. RESULTS: As of April 2019, data collection for Phase I is complete and data analysis is underway. These data will inform the list of outcomes to be advanced into Phase II. Recruitment for Phase II will commence in June 2019, and it is anticipated that it will take 6 months to complete the three-step consensus process and identify a provisional core outcome set. The study results are expected to be published in early 2020. CONCLUSIONS: Expert consensus-driven recommendations on outcome measurement will facilitate the inclusion of survivorship outcomes considered important by cancer survivors and health professionals in future research. Adoption of the core outcome set will enable comparison and synthesis of evidence across studies and enhance the quality of PRO data collected in cancer survivorship research, particularly when applied to address macro-level questions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/14544.
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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 | 0.026 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".