Triple-nerve decompression surgery for the treatment of painful diabetic peripheral neuropathy in lower extremities: A study protocol for a randomized controlled trial
Bibliographic record
Abstract
Objectives Painful diabetic peripheral neuropathy (DPN) is often refractory to conventional medications. Triple-nerve decompression was proposed for painful DPN due to the frequent involvement of multiple nerve entrapments in diabetes. However, the role of decompressive surgery remains controversial. This trial aims to assess the efficacy of triple-nerve decompression for patients with painful DPN suggestive of nerve entrapment using a randomized controlled trial (RCT) design. Methods and analysis This trial is a single-center RCT and will be conducted in Shanghai Ninth People's Hospital. Enrolled subjects (n= 74) with painful DPN due to nerve compression, which can be detected by nerve conduction studies, will be randomly allocated at a 1:1 ratio into surgical and non-surgical groups. The primary outcome will be measured by 50% responder rates, which is defined as the proportion of subjects with at least 50% reduction of the mean weekly visual analog score (VAS) of pain from baseline after 6 months of treatment. Mean weekly VAS will be additionally evaluated 1 week (W1), 1 month (M1), and 3 months (M3) after treatment to monitor the changes in pain intensity. The secondary outcomes include two-point discrimination (TPD), Toronto clinical scoring system (TCSS), electrophysiological indexes, hospital anxiety and depression scale (HADS), and the medical outcome study short-form 36-item questionnaire (SF-36). A quantitative analgesic questionnaire (QAQ) will be used as a secondary outcome to quantify the analgesic medication weekly. TPD and TCSS will be conducted at W1, M1, M3, and M6 after treatment. Electrophysiological tests, HADS, and SF-36 will be performed at M3 and M6. Ethics and dissemination Ethics approval has been obtained from the Ethics Committee of Shanghai Ninth People's Hospital (SH9H-2-21-T323-2). It was registered on the Chinese Clinical Trial Registry website ( http://www.chictr.org.cn ) on 16 August 2021 with the number ChiCTR2100050049. Written informed consent will be obtained from all participants. The results of this trial will be disseminatedviapeer-reviewed journals, mass media, and presentations at national and international academic conferences.
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 | 0.026 | 0.024 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.053 | 0.008 |
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".