Neural Networks: A Diagnostic Tool for Gastric Electrical Uncoupling?
Bibliographic record
Abstract
Neural Networks have been successfully employed in different biomedical settings. They have been\nuseful for feature extractions from images and biomedical data in a variety of diagnostic applications. In this\npaper, they are applied as a diagnostic tool for classifying different levels of gastric electrical uncoupling in\ncontrolled acute experiments on dogs. Data was collected from 16 dogs using six bipolar electrodes inserted into\nthe serosa of the antral wall. Each dog underwent three recordings under different conditions: (1) basal state, (2)\nmild surgically-induced uncoupling, and (3) severe surgically-induced uncoupling. For each condition half-hour\nrecordings were made. The neural network was implemented according to the Learning Vector Quantization\nmodel. This is a supervised learning model of the Kohonen Self-Organizing Maps. Majority of the recordings\ncollected from the dogs were used for network training. Remaining recordings served as a testing tool to examine\nthe validity of the training procedure. Approximately 90% of the dogs from the neural network training set were\nclassified properly. However, only 31% of the dogs not included in the training process were accurately\ndiagnosed. The poor neural-network based diagnosis of recordings that did not participate in the training process\nmight have been caused by inappropriate representation of input data. Previous research has suggested\ncharacterizing signals according to certain features of the recorded data. This method, if employed, would reduce\nthe noise and possibly improve the diagnostic abilities of the neural network.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".