Treatment goal setting for complex patients: protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: An increasing number of people are living longer with multiple health and social care needs, and may rely heavily on health system resources. When dealing with multiple conditions, patients, caregivers and healthcare providers (HCPs) often experience high treatment burden due to unclear care trajectories, a myriad of treatment decisions and few guidelines on how to manage care needs. By understanding patient and caregiver priorities, and setting treatment goals, HCPs may help improve patient outcomes and experiences. This study aims to examine the extent and nature of the literature on treatment goal setting in complex patients, identify gaps in evidence and areas for further inquiry and guide a research programme to develop definitions, measures and recommendations for treatment goal setting. METHODS AND ANALYSIS: This study protocol outlines a scoping review of the peer reviewed and the grey literature, using established scoping review methodology. Literature will be identified using a multidatabase and grey literature search strategy developed by two librarians. Papers and reports on the topic of goal setting that address complexity or complex patients will be included. Results of the search will be screened independently by two reviewers and included studies will be abstracted and charted in duplicate. ETHICS AND DISSEMINATION: Ethics approval is not required for this scoping review. Working with the knowledge users on the team, we will prepare educational materials and presentations to disseminate study findings to HCPs, caregivers and patients, and at relevant national and international conferences. Results will also be published in a peer-reviewed journal.
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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 | 0.126 | 0.120 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.090 | 0.019 |
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".