SCHeLTI Platform: an Intervention Support System Tailored for a Randomized Controlled Trial (Preprint)
Bibliographic record
Abstract
BACKGROUND With the rapid development of eHealth technologies, the convenient exchange of health-related electronic data can promote interactive exchange of information between healthcare providers (HCPs) and patients, making the communication between doctors and patients more coordinated and transparent. The Sino-Canada Healthy Life Trajectories Initiative (SCHeLTI) study is an ongoing randomized controlled trial to evaluate the effectiveness of a multifaceted, community-family-mother-child intervention on childhood overweight and obesity (OWO). A management system to support the SCHeLTI interventions needs to be developed. OBJECTIVE Considering the need for a supporting system to facilitate the implementation of interventions and the exchange of information between HCPs and participants, the SCHeLTI platform was designed and developed with the aim to facilitate the context-specific interventions in the SCHeLTI study. METHODS We tailored the SCHeLTI platform to the specifics of the SCHeLTI study. Multiple professional background experts were involved in the process of building the application, including the participation of personnel with medical professional background, clinical trials coordination and computer science. In the pilot phase, we collected feedback from HCPs and participants in the use process to further optimize the product. RESULTS The SCHeLTI platform includes the interworking and interconnection between the participants' mobile phone and the HCP's computers. A mobile application and a Web based management system were designed. The participant's terminal (the SCHeLTI APP) was successfully implemented and fully integrated into the intervention programme. The computer terminal managed by the research team create an innovative support environment that guides the participants toward healthy lifestyle changes. CONCLUSIONS A technically advanced and web-based management terminal and mobile phone app corresponding to the SCHeLTI needs were developed and used in the SCHeLTI study. CLINICALTRIAL trial registration No. ChiCTR1800017773
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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 | 0.047 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.116 | 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".