Three-dimensional static parametric modelling of phasic colonic contractions for the purpose of microprocessor-controlled functional stimulation
Bibliographic record
Abstract
The study aimed at creating an integrated electromechanical model of invoked phasic contractions in canine colon during direct high frequency voltage stimulation. The model utilized data obtained from two large anaesthetized dogs that underwent laparotomy and serosal implantation of two circumferential electrode pairs into a distal segment of the left colon. The strength distribution of the stimulating electric field was analysed over a cylindrical mesh-surface grid modelling the interrogated colonic segment. Recordings of the stimulating current were utilized to model smooth muscle depolarization using linearized macroscopic tissue conductivity. The invoked contractile stress was related to the stimulating electric field strength using an exponential sigmoid function. Artificially produced occlusion of the lumen was derived for a pair of 5mm electrodes positioned on a cylindrical mesh-surface of 2 cm diameter and 15 cm length. The model simulated contractions invoked by stimuli of different amplitude (up to 12 V) with 98.6% accuracy of approximation. Macroscopic tissue conductivity was modelled as a combination of two first-order exponential terms involving a 3ms time constant. Real-time simulation of the current drawn by the smooth muscle during 10 V/50Hz bipolar voltage stimulation was performed. The integrated electromechanical model facilitates the quantification of microprocessor-controlled phasic colonic contractions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".