Exploring Transitions in Care Among Patients With Head and Neck Cancer: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Individuals living with and beyond head and neck cancer (HNC) experience many transitions in care (TiC), as their treatment and care involve a team of multidisciplinary healthcare providers across a variety of settings. TiC can be associated with medical error, patient dissatisfaction with care, and overuse of healthcare resources. The objective of this study is to understand TiC among individuals living with and beyond HNC by mapping and characterizing the existing evidence. METHODS: This scoping review identified evidence sources describing TiC among individuals living with and beyond HNC by searching five medical research databases using structured language and keywords related to the population (cancer) and concept (TiC). Titles and abstracts, and full texts were screened in duplicate for eligibility. Eligible studies were those that described or evaluated TiC among individuals with HNC, of any study design published in any language without restriction based on the date of publication. Quantitative data were summarized using descriptive statistics, and qualitative data were synthesized using thematic analysis. RESULTS: The search identified 26,431 unique evidence sources, of which 3375 were screened in full-text, and 57 were included. Most studies were conducted in the United States between 2001 and 2024 and were retrospective cohort studies. Included evidence sources most frequently focused on the delays in the transition from diagnosis to treatment, followed by the TiC from surgery to radiation. The majority of evidence sources reported system-level outcomes followed by patient-level outcomes. Eight evidence sources described interventions to improve TiC. CONCLUSION: The study identified a gap in our evidence regarding transitions during active treatment and in evaluating interventions to improve TiC among individuals living with and beyond HNC. Future research should focus on bridging these gaps to improve TiC and consequently outcomes for individuals living with and beyond HNC.
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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 | 0.024 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.022 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".