Methods of investigation for cardiac autonomic dysfunction in human research studies
Bibliographic record
Abstract
This consensus document provides evidence-based guidelines regarding the evaluation of diabetic cardiovascular autonomic neuropathy (CAN) for human research studies; the guidelines are the result of the work of the CAN Subcommittee of the Toronto Diabetic Neuropathy Expert Group. The subcommittee critically reviewed the limitations and strengths of the available diagnostic approaches for CAN and the need for developing new tests for autonomic function. It was concluded that the most sensitive and specific approaches currently available to evaluate CAN in clinical research are: (1) heart rate variability, (2) baroreflex sensitivity, (3) muscle sympathetic nerve activity, (4) plasma catecholamines, and (5) heart sympathetic imaging. It was also recommended that efforts should be undertaken to develop new non-invasive and safe CAN tests to be used in clinical research, with higher sensitivity and specificity, for studying the pathophysiology of CAN and evaluating new therapeutic approaches.
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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 | 0.287 | 0.234 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".