Utility of electronic decision-support tools for patients with head and neck cancer: A scoping review
Bibliographic record
Abstract
The objective of this scoping review is to evaluate the range and nature of electronic decision-support tools that have been researched and/or trialled for patients with head and neck cancer (HNC), and to explore the implication on patient safety through improving risk communication. A scoping review was conducted by: (1) identifying the research question; (2) identifying relevant studies; (3) study selection; (4) collecting data and data items; (5) and reporting on results. Six databases, reference lists and grey literature from Google and Google Scholar were searched, including articles in English from January 2010 to January 2021. Articles discussing electronic decision aids (DAs) for oncology patients were searched then sorted by specificity for HNC. This returned 4217 articles for oncology but only 167 for HNC. Twelve articles met the inclusion criteria and were incorporated in the analysis. Five DAs have been created with varying design characteristics but four consistent themes: appreciation for DAs, usefulness of visuals, impact on reducing decisional conflict and anxiety while increasing knowledge, satisfaction, and shared decision-making, and the variability of patient information needs. Findings demonstrate the paucity of developed and/or trialled electronic DAs for patients with HNC and confirms their value and need for further research and development in this area.
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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 | 0.016 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".