Palliative care interventions for patients with head and neck cancer: protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: A head and neck cancer (HNC) diagnosis significantly impacts a patient's quality of life (QOL). Palliative care potentially improves their QOL. We will conduct a scoping review to identify existing knowledge about palliative care interventions for patients with HNC. METHODS AND ANALYSIS: This scoping review was designed in accordance with the JBI Manual for Evidence Synthesis: Scoping Reviews and will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. Our eligibility criteria follow the Population, Intervention, Comparison or Control, Outcomes and Study characteristics framework. The population is adult patients with locally advanced, metastatic, unresectable and/or recurrent HNC. We include peer-reviewed journal articles and articles in the press, in English, reporting on palliative care interventions with at least two of the eight National Consensus Project on Clinical Practice Guidelines for Quality Palliative Care domains; studies with and without comparators will be included. The outcomes are patient QOL (primary) and symptom severity, patients' satisfaction with care, patients' mood, advance care planning and place of death (secondary). We developed a search strategy across ten databases, to be searched from the inception to 11 September 2023: Medline ALL (Medline and EPub Ahead of Print and In-Process, In-Data-Review & Other Non-Indexed Citations), Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, Embase Classic+Embase, Emcare and PsycINFO all from the OvidSP platform; CINAHL from EBSCOhost, Scopus from Elsevier, Web of Science from Clarivate and Global Index Medicus from WHO. We will extract data using a piloted data form and analyse the data through descriptive statistics and thematic analysis. ETHICS AND DISSEMINATION: Ethics approval is not needed for a scoping review. We will disseminate the findings to healthcare providers and policy-makers by publishing the results in a scientific 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 | 0.083 | 0.063 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.016 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.095 | 0.016 |
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".