OTsENKA VYRAZhENNOSTI AVTONOMNOY KARDIAL'NOY NEYROPATII PRI SAKhARNOM DIABETE 1-go TIPA METODOM RITMOKARDIOGRAFII
Bibliographic record
Abstract
Aim. To assess manifestation of autonomic cardial neuropathy (ACNP) in type 1 diabetes mellitus using rhythmocardiography method. Materials and methods. The study included 147 patients with type 1 DM, 87 men and 61 women with a mean age of 36,3±13,0 years. Duration and age of DM1 onset was 11,1±10,3 and 24,8±12,3 years. HbA1c – 9,9+2,5%. ACNP diagnosis was confirmed by The Toronto Consensus Panel on Diabetic Neuropathy Criteria with assessment of heart rate variability (HRV) indices using rhythmocardiography (RCG) method and apparatus-program complex “Micor”. The control group included 41 persons. Data processing was performed by means of STATISTICA 6.0 and SPSS 17.0 for Windows. Results. Significant ( p <0,001) fall in HRV indices among DM1patients versus the control was revealed: general variability – SDNN (c) 0,026±0,017 and 0,058±0,01, amplitude of respiratory arrhythmia ARA (c) 0,037±0,03 and 0,095±0,03, root-mean-square deviation in three types of waves – humoral-metabolic σ l (c) 0,015±0,01 and 0,028±0,01, sympathetic σ m (c) 0,012±0,01 and 0,025±0,01, parasympathetic σ s (c) 0,014±0,01 and 0,042±0,02; sample reactivity was decreased. ACNP was diagnosed in 35 (24%) patients including 15 (10%) patients with severe stage 3 ACNP, 10 (6,8%) – stage 2, 12 (8%) – stage 1. Conclusion. Marked disorders of peripheral HRV regulation caused by inhibition of reflex sympathy-parasympathetic regulation in sinus node of the heart were detected with RCG method. ACNP was observed among the most critical patients. The basic factors of ACNP worsening were DM duration and RCG-parameters of reaction to the effect of stimulus.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 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 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".