Stress ECG and Laboratory Database for the Assessment of Diabetic Cardiovascular Autonomic Neuropathy
Bibliographic record
Abstract
Development of a diabetic patient database in order to study Cardiovascular Autonomic Neuropathy (CAN) using as a primary source, stress ECG is presented. The selected platform (ecgML) allows user-friendly environment to analyze and interpret graphs, signals and data. It also allows the ability to perform annotations and reports done by users from different fields. In order to feed the database, the input data is codify using MatLab. The database is composed by two populations: 1) Type 2 Diabetes mellitus group and 2) a control group with no medical history of cardiovascular disease. At the present, there are 62 records available from these two groups. The database also contains laboratory parameters, concurrent medical diagnoses reports verified by cardiologists and other clinicians, automatic annotations for each beat and trend series from parameters extracted from the ECG signals such as RR intervals and ST segment measurements. All this information will become very useful for CAN investigations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.006 | 0.003 |
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".