Fully automated analysis of colonic manometry recordings
Bibliographic record
Abstract
The purpose of this study was to develop a computer program for fully automated analysis of all presently known motor patterns in human colonic motility recordings. Colonic pressure recordings obtained from 24 healthy volunteers were used. Algorithms were developed for the detection and numerical analysis of five types of pressure waves: antegrade, retrograde, simultaneous, high-amplitude and isolated pressure waves. Furthermore, periodical motor activity was quantified. Validation was performed by comparison with visual analysis by two experienced observers. Patterns recorded during day- and night-time were compared using multiple-factor analysis of variance with Bonferroni correction. Automated analysis correlated well with visual peak detection (r = 0.98, P <0.01) and detection of antegrade pressure waves (r = 0.98, P <0.01). Most motor patterns showed a diurnal variation. During the night, prevalences of antegrade (938 vs 455; P <0.05), retrograde (112 vs 81; P <0.05), high-amplitude (12.9 vs 1.3; P <0.05), isolated pressure waves (1114 vs 765; P <0.05), and periodic motor activity were decreased (7.33 vs 4.47%; P <0.05). However, when expressed as percentage of absolute numbers of pressure waves, prevalences remained constant. In conclusion, fully automated analysis of all hitherto described colonic motility patterns is feasible. During the night, overall wave prevalences markedly decreased, but the distribution over the various motor patterns was preserved.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".