A Neurosurgeon's Guide to Mobile Health Application Development With a Case Study for Cervical Myelopathy
Bibliographic record
Abstract
The integration of mobile health (mHealth) technologies is transforming neurosurgery. Despite its potential, many uses remain unrealized due to the unique challenges and complexity of developing mHealth technology. While neurosurgeons bring invaluable clinical expertise and an understanding of patient needs, the technical intricacies of application development often require collaboration with developers and computer scientists, a process that can feel unfamiliar and difficult to navigate. The aim of this article was to demystify mHealth development by providing a guide for neurosurgeons seeking to develop disease-specific mHealth applications. We outline this process using the development of SynapTrack, an mHealth tool designed to provide objective assessments of degenerative cervical myelopathy, a chronic condition caused by symptomatic compression of the cervical spinal cord. This article offers neurosurgeons concrete guidance on navigating key considerations such as design decisions, algorithm integration, database architecture, and data security. By grounding these insights with SynapTrack, this guide offers a transparent view into the development process and provides a practical framework that can be adapted to the development of other mHealth tools. The aim of this work was to foster interdisciplinary collaboration and enable neurosurgeons to develop mHealth applications tailored to the specific needs of their specialty.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.013 |
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".