Clinical prediction tools for patient-reported outcomes in gastrointestinal cancer: a scoping review protocol
Bibliographic record
Abstract
BACKGROUND: Gastrointestinal (GI) cancers are among the most significant contributors to the global cancer burden, causing substantial physical and emotional distress. Effective management of patient-reported outcomes (PROs) is essential for enhancing quality of life and overall survival in cancer care. Despite significant advances in cancer care, understanding PROs and their integration into clinical practice remains limited. Prediction models for PROs have the potential to support patient-centred care by improving shared decision-making and informing care plans. However, the development and application of clinical tools that predict PROs in patients with GI cancer have not been systematically explored. This scoping review aims to explore clinical prediction tools for PROs and the quality of life in patients with GI cancer, identifying current tools, predictors and outcomes, as well as evaluating their clinical usability and equity considerations. METHODS AND ANALYSIS: A scoping review methodology, guided by the JBI Manual for Evidence Synthesis and the Arksey and O'Malley framework, will be used. The review will include studies of adult patients with primary GI cancer that developed or validated clinical prediction tools for PROs or quality of life. Inclusion criteria require the use of self-reported PRO measures. A systematic search of Ovid Medline, Embase and CINAHL will be conducted from 1946 to 2024. The search strategy will be updated periodically to incorporate the most recent literature and complemented by hand-searching references. Data extraction will focus on tool characteristics, predictors, statistical methods and equity considerations. The findings will be synthesised descriptively, mapping trends, identifying gaps and highlighting areas for future research. ETHICS AND DISSEMINATION: Ethical approval is not required for this literature-based study. Results will be disseminated through peer-reviewed publications, conferences and patient advocacy networks to maximise the impact on research, policy and clinical practice.
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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 | 0.096 | 0.090 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.074 | 0.014 |
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".