The dilemma of diabetes in chronic inflammatory demyelinating polyneuropathy
Bibliographic record
Abstract
PURPOSE: We reviewed the literature on chronic inflammatory demyelinating polyneuropathy (CIDP) in diabetes mellitus (DM) and explored real-world data on the prevalence and treatment of CIDP within DM. METHODS: A literature search of Scopus was performed for the terms chronic inflammatory demyelinating polyradiculoneuropathy, chronic inflammatory demyelinating polyneuropathy, CIDP, and prevalence, incidence, epidemiology, or diabetes; peripheral neuropathy and prevalence or diabetes. We also searched through the reference lists of the resulting publications for additional findings that may have been missed. Additional publications on guidelines for the diagnosis of CIDP and diabetic neuropathy were also included. A descriptive analysis of the 2009-2013 PharMetrics Plus™ Database was performed to estimate the prevalence and treatment of CIDP within the DM population. RESULTS: There is an increasing body of literature suggesting that the prevalence of CIDP tends to be higher in diabetic patients, especially in those of older age. Our real-world data seem to support published findings from the literature. For the total cohort (N=101,321,694), the percent prevalence of CIDP (n=8,173) was 0.008%; DM (n=4,026,740) was 4%. The percent prevalence of CIDP without DM (n=5,986) was 0.006%; CIDP with DM (n=2,187) was 9-fold higher at 0.054%. For patients >50years old, there was a significantly higher percentage of CIDP with DM than CIDP without DM. Approximately 50% of CIDP patients were treated with IVIg, 23%-24% with steroids, 1%-2% with PE, and 20%-23% received no treatment. CONCLUSIONS: In addition to the growing evidence of higher prevalence of CIDP in DM, our findings reinforce the need for heightened awareness of the association of CIDP and DM.
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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 | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".