Digital Technology in Somatic and Gene Therapy Trials of Pediatric Patients With Ocular Diseases: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Pharmacogenomics suggests that diseases with similar symptomatic presentations often have varying genetic causes, affecting an individual patient's response to a specific therapeutic strategy. Gene therapies and somatic cell therapies offer unique therapeutic pathways for ocular diseases and often depend on increased understanding of the genotype-phenotype relationship in disease presentation and progression. While demand for personalized medicine is increasing and the required molecular tools are available, its adoption within pediatric ophthalmology remains to be maximized in the postgenomic era. OBJECTIVE: The objective of our study was to address the individual hurdles encountered in the field of genomic-related clinical trials and facilitate the uptake of personalized medicine, we propose to conduct a review that will examine and identify the digital technologies used to facilitate data analysis in somatic and gene therapy trials in pediatric patients with ocular diseases. METHODS: This paper aims to present an outline for Healthcare Information Technology and Information and Communication Technology resources used in somatic and gene therapy clinical trials in children with ocular diseases. This review will enable authors to identify challenges and provide recommendations, facilitating the uptake of genetic and somatic therapies as therapeutic tools in pediatric ophthalmology. The review will also determine whether conducting a systematic review will be beneficial. RESULTS: Database searches will be initiated in September 2018. We expect to complete the review in December 2019. CONCLUSIONS: Based on review findings, the authors will summarize methods used for facilitating IT integration in personalized medicine. Additionally, it will identify further research gaps and determine whether conducting further reviews will be beneficial. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/10705.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".