Manometry-Based Cough Identification Algorithm
Bibliographic record
Abstract
Gastroesophageal reflux disease (GERD) is a common cause of chronic cough. For the diagnosis and\ntreatment of GERD, it is desirable to quantify the temporal correlation between cough and reflux events. Cough\nepisodes can be identified on esophageal manometric recordings as short-duration, rapid pressure rises. The\npresent study aims at facilitating the detection of coughs by proposing an algorithm for the classification of cough\nevents using manometric recordings. The algorithm detects cough episodes based on digital filtering, slope and\namplitude analysis, and duration of the event. The algorithm has been tested on in vivo data acquired using a\nsingle-channel intra-esophageal manometric probe that comprises a miniature white-light interferometric fiber\noptic pressure sensor. Experimental results demonstrate the feasibility of using the proposed algorithm for\nidentifying cough episodes based on real-time recordings using a single channel pressure catheter. The\npresented work can be integrated with commercial reflux pH/impedance probes to facilitate simultaneous 24-hour\nambulatory monitoring of cough and reflux events, with the ultimate goal of quantifying the temporal correlation\nbetween the two types of events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| 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".