Reconstructive Surgery in a Patient with High Radial Nerve Palsy Using the WALANT Technique
Bibliographic record
Abstract
Tendon transfers can be tied too tight or too loose. Both impede good function after surgery. Performing tendon transfers without sedation and pain during the surgery and then watching the patient move the transfer have helped us adjust the tension more accurately. This method can be applied to complex transfers such as radial nerve palsy triple tendon transfers. We describe the technique and results of a triple tendon transfer using wide-awake local anesthesia no tourniquet in a patient with a high radial nerve palsy. This was a complex case of reconstruction after five operations at the level of the humerus. This left him with a pseudoarthrosis of the humerus and a complete radial nerve palsy. We performed tendon transfers of pronator teres to extensor carpi radialis brevis, flexor carpi ulnaris to extensor digitorum communis, and palmaris longus to extensor pollicis longus tendons. Eighteen months after the triple tendon transfer surgery for the radial nerve palsy, the patient has good extension of the fingers, wrist, and thumb. He can open and close the hand properly. He has excellent function and mobility allowing him to perform most activities in a manner that is practically normal. Wide-awake local anesthesia no tourniquet can be used safely and successfully in complex cases requiring triple radial nerve tendon transfers of pronator teres to extensor carpi radialis brevis, flexor carpi ulnaris to extensor digitorum communis, and palmaris longus to extensor pollicis longus tendons.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".