A New Approach to Analysis and Modeling of Esophageal Manometry Data in Humans
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we propose a new approach to the analysis and modeling of esophageal manometry (EGM) data to assist the diagnosis of esophageal motility disorders in humans. The proposed approach combines three techniques, namely, wavelet decomposition (WD), nonlinear pulse detection technique (NPDT), and statistical pulse modeling. Specifically, WD is applied to the filtering of the EGM data, which is contaminated with electrocardiography (ECG) artifacts. A new NPDT is applied to the denoised data leading to identification and extraction of diagnostically important information, i.e., esophageal pulses from the respiration artifacts. Such information is used to generate a statistical model that can classify the EGM patterns. The proposed approach is computationally effortless, thus making it suitable for real-time application. Experimental results using measured EGM data of 20 patients, including ten abnormal cases is presented. Comparison of our results with those from existing techniques illustrates the advantages of the proposed approach in terms of accuracy and efficiency.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 it