Chemotherapy-Induced Neuropathy and Diabetes: A Scoping Review
Bibliographic record
Abstract
Although cancer and diabetes are common diseases, the relationship between diabetes, neuropathy and the risk of developing peripheral sensory neuropathy while or after receiving chemotherapy is uncertain. In this review, we highlight the effects of chemotherapy on the onset or progression of neuropathy in diabetic patients. We searched the literature in Medline and Scopus, covering all entries until 31 January 2021. The inclusion and exclusion criteria were: (1) original article (2) full text published in English or Spanish; (3) neuropathy was specifically assessed (4) the authors separately analyzed the outcomes in diabetic patients. A total of 259 papers were retrieved. Finally, eight articles fulfilled the criteria, and four more articles were retrieved from the references of the selected articles. The analysis of the studies covered the information about neuropathy recorded in 768 cancer patients with diabetes and 5247 control cases (non-diabetic patients). The drugs investigated are chemotherapy drugs with high potential to induce neuropathy, such as platinum derivatives and taxanes, which are currently the mainstay of treatment of various cancers. The predisposing effect of co-morbid diabetes on chemotherapy-induced peripheral neuropathy depends on the type of symptoms and drug used, but manifest at any drug regimen dosage, although greater neuropathic signs are also observed at higher dosages in diabetic patients. The deleterious effects of chemotherapy on diabetic patients seem to last longer, since peripheral neuropathy persisted in a higher proportion of diabetic patients than non-diabetic patients for up to two years after treatment. Future studies investigating the risk of developing peripheral neuropathy in cancer patients with comorbid diabetes need to consider the duration of diabetes, cancer-induced neuropathic effects per se (prior chemotherapy administration), and the effects of previous cancer management strategies such as radiotherapy and surgery.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".