Evaluation of interoceptive accuracy in diabetic individuals with or without polyneuropathy
Bibliographic record
Abstract
Aim: Diabetic peripheral neuropathy (DPN) is a heterogeneous disease with a complex pathophysiology that can affect both autonomic and somatic components of the nervous system. Interoception is a perceptual and cognitive concept expressing the internal sensory perception that evaluates signals from the body and internal organs. Interoceptive abilities have been indicated to be reduced in various chronic pain syndromes and chronic neuropathies. That said, interoceptive skills in individuals with Type 2 Diabetes Mellitus with and without a previous DPN comorbidity have not been comparatively examined. We aimed to examine whether there is a difference in terms of interoceptive accuracy in individuals diagnosed with Type 2 Diabetes Mellitus with and without DPN for the first time. Methods: 20 individuals with a diagnosis of Type 2 Diabetes Mellitus with a co-diagnosis of DPN and 20 individuals with a diagnosis of Type 2 Diabetes Mellitus without a co-diagnosis of DPN were recruited in the Electroneurophysiology Laboratory of Muğla Sıtkı Koçman University. The presence of DPN was evaluated with both Toronto Clinical Scoring System and electromyographic examination. General cognitive status was evaluated with the Mini-Mental State Examination, general psychiatric status with the Patient Health Questionnaire-9, and cardiac interoceptive accuracy with the Heartbeat Counting Test. Results: No difference was found in terms of cardiac interoceptive accuracy in individuals with Type 2 Diabetes Mellitus with and without DPN. Conclusions: The potential decrease in cardiac interoception might be related to chronic pain or autonomic neuropathy rather than the presence of DPN. Studies examining interoception in these subgroups are required.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".