Assessing the Quality, Feasibility, and Efficacy of Electronic Patient Platforms Designed to Support Adolescents and Young Adults With Cancer: A Systematic Review Protocol
Bibliographic record
Abstract
BACKGROUND: A range of innovative websites, mobile technologies, eHealth and mHealth platforms have emerged to support adolescents and young adults (AYAs) with cancer. Previous reviews have identified these various applications and solutions, but no review has summarized the quality, feasibility, and efficacy of existing patient platforms (inclusive of websites, mobile technologies, mHealth and eHealth platforms) developed specifically for young people with cancer. OBJECTIVE: This paper describes the design of a protocol to conduct a review of published studies or reports which describe or report on an existing platform designed specifically for AYAs who have had a cancer diagnosis. METHODS: A search string was developed using a variety of key words and Medical Subject Heading and applied to bibliographic databases. General data (sample characteristics, patient platform development, design and, if applicable, pilot testing outcomes) will be extracted from reports and studies. Drawing on a previously developed coding schematic, the identified patient platforms will be coded for mode of delivery into (1) automated functions, (2) communicative functions, and (3) use of supplementary modes. An adapted version of the Mobile App Rating Scale (MARS) will be used to assess the of quality of each identified patient platform. The methodological quality of included studies will be assessed using the Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields (QualSyst). Both authors will independently screen eligible studies for final inclusion and will both be responsible for data extraction and appraisal. Data will be synthesized narratively to provide an overview of identified patient platforms. RESULTS: The review began in October 2016 and is currently in progress. The review paper will be submitted for peer-review and publication in the summer of 2017. CONCLUSIONS: This review will be unique in its focus on assessing, where possible, the quality and efficacy of patient platforms for adolescents and young adults diagnosed with cancer. Results generated from this review will provide an invaluable insight into the utility of modern technology in supporting young people with cancer.
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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 | 0.148 | 0.147 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.015 | 0.016 |
| Bibliometrics | 0.020 | 0.015 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.048 | 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".