Development of an Algorithm to Perform a Comprehensive Study of Autonomic Dysreflexia in Animals with High Spinal Cord Injury Using a Telemetry Device
Bibliographic record
Abstract
Spinal cord injury (SCI) is a debilitating neurological condition characterized by somatic and autonomic dysfunctions. In particular, SCI above the mid-thoracic level can lead to a potentially life-threatening hypertensive condition called autonomic dysreflexia (AD) that is often triggered by noxious or non-noxious somatic or visceral stimuli below the level of injury. One of the most common triggers of AD is the distension of pelvic viscera, such as during bladder and bowel distension or evacuation. This protocol presents a novel pattern recognition algorithm developed for a JAVA platform software to study the fluctuations of cardiovascular parameters as well as the number, severity and duration of spontaneously occurring AD events. The software is able to apply a pattern recognition algorithm on hemodynamic data such as systolic blood pressure (SBP) and heart rate (HR) extracted from telemetry recordings of conscious and unrestrained animals before and after thoracic (T3) complete transection. With this software, hemodynamic parameters and episodes of AD are able to be detected and analyzed with minimal experimenter bias.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".