Seizure Detection and Tracking Through Portable Technologies: Protocol for a Systematic Review
Bibliographic record
Abstract
Abstract BackgroundEpilepsy is a chronic neurological disease characterized by recurrent seizures due to excessive neuronal discharges and abnormal neuronal discharges. 30-40% of them are pharmaco-resistant, which is associated with cognitive impairment. seizure-related injuries and a higher risk of sudden unexpected death in epilepsy (SUDEP). The only way to minimize the risk of SUDEP is having a control of seizures. However, epileptics living or get a seizure alone can’t report the exact information to their physicians. Then, the level of gravity of their disease is inexact. Currently, the only way to identify a seizure is by electroencephalography (EEG), used by healthcare resources. It requires medical staff for its installation and interpretation. Nonetheless, a lot of new technologies developed for recording seizures at home have been developed. These one hasn’t optimally included epileptics and only a few went to a clinical trial; thus, scientific evidence is limited regarding their effectiveness. MethodsThe methodological approach is based in the Cochrane handbook for conducting systematic review. All studies in any languages about the technologies detecting seizures will be compared for any type of epilepsy and for any outcome measured. Two reviewers will independently screen the results for MEDLINE [PubMed, 1951-June 2019], EMBASE [OVID, 1947-June 2019], the Cochrane Register of Controlled Trails (CENTRAL) [up to June 2019] and grey literature [up to June 2019]. Extracted data will be recorded, categorized and summarized.DiscussionThe expected results would be the too short time of the trials and many positive false to get a conclusion of the effectiveness of the technologies studied. This systematic review is needed in order to improve knowledge of the effectiveness of technologies for detecting seizures and use this information for the development of a personalized tool for detecting seizures on the basis of the best available scientific evidence.Systematic review registrationPROSPERO CRD42020129787.
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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 | 0.048 | 0.070 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.026 | 0.020 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.085 | 0.009 |
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".