Assessments and interventions for patient-centered communication in older adults with cancer: a scoping review protocol
Bibliographic record
Abstract
Although cancer incidence increases with age, older adults remain underrepresented in clinical trials, resulting in limited evidence to guide their care. Given the heterogeneity of this population, individualized, patient-centered approaches are essential. PCC is critical across all stages of cancer care, including screening, diagnosis, treatment, survivorship, and end-of-life. The 2007 National Cancer Institute (NCI) monograph defines PCC through six core functions: fostering healing relationships, exchanging information, responding to emotions, managing uncertainty, making decisions, and enabling self-management. While prior reviews have focused primarily on specific aspects of PCC, such as shared decision-making, the scope of this review is broader. Guided by the NCI framework, we aim to map how PCC with older adults with cancer has been conceptualized, assessed, and addressed across the literature. This includes identifying which PCC domains are evaluated or targeted in interventions, exploring methodological approaches, and highlighting key gaps to inform the development of comprehensive assessment strategies and evidence-based interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.012 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.063 | 0.008 |
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".