Percutaneous Peripheral Nerve Stimulation to Control Postoperative Pain, Decrease Opioid Use, and Accelerate Functional Recovery Following Orthopedic Trauma
Bibliographic record
Abstract
Orthopedic trauma is a significant military problem, causing several of the most disabling conditions with high rates of separation from duty and erosion of military readiness. The objective of this report is to summarize the findings of case series of a non-opioid therapy-percutaneous peripheral nerve stimulation (PNS) - and describe its potential for postoperative analgesia, early opioid cessation, and improved function following orthopedic trauma. Percutaneous PNS has been evaluated for the treatment of multiple types of pain, including two case series on postoperative pain following total knee replacement (n = 10 and 8, respectively) and a case series on postamputation pain (n = 9). The orthopedic trauma induced during TKR is highly representative of multiple types of orthopedic trauma sustained by Service members and frequently produces intense, prolonged postoperative pain and extended opioid use following surgery. Collectively, the results of these three clinical studies demonstrated that percutaneous PNS can provide substantial pain relief, reduce opioid use, and improve function. These outcomes suggest that there is substantial potential for the use of percutaneous PNS following orthopedic trauma.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".