Mechanosensitivity during straight leg raise and slump neurodynamic tests in people with type 1 diabetes mellitus and diabetic peripheral neuropathy
Bibliographic record
Abstract
Objectives Neurodynamic tests are clinical tests used to identify heightened nerve mechanosensitivity but may be negative in the presence of severe neuropathy, as seen in people with carpal tunnel syndrome and type 2 diabetes. It is not known if this also occurs in people with diabetic peripheral neuropathy (DPN) from type 1 diabetes mellitus (T1DM). The primary aim of this study is to determine the proportion of positive neurodynamic tests in people with T1DM and DPN. The secondary aim is to assess whether the severity of DPN influences the presence of a positive neurodynamic test.Methods This is a cross-sectional study. Forty-three participants with T1DM and DPN were assessed using straight leg raise (SLR) and slump neurodynamic tests to determine a positive and negative test. DPN severity was graded according to Toronto Clinical Scoring System (TCSS).Results Forty-six percent and 56% of participants had positive SLR and slump tests, respectively, indicating heightened nerve mechanosensitivity. There was a statistically significant association between negative neurodynamic tests and DPN severity (p < 0.0001). In addition, participants with negative neurodynamic tests had significantly higher TCSS scores compared to participants with positive neurodynamic tests (p < 0.0001).Discussion/Conclusion People with T1DM and severe DPN, as graded by TCSS, are more likely to demonstrate negative neurodynamic tests than those with mild DPN. Future studies should investigate the relationship between neurodynamic tests and nerve function in other conditions. This supports previous research on negative neurodynamic tests in severe neuropathy, suggesting that neurodynamic tests should not be used alone to determine nerve involvement.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".