Nerve injuries do occur in elbow arthroscopy
Bibliographic record
Abstract
PURPOSE: The purpose is to create more awareness as well as emphasize the risk of permanent nerve injury as a complication of elbow arthroscopy. METHODS: Patients who underwent elbow arthroscopy complicated by permanent nerve injury were retrospectively collected. Patients were collected using two strategies: (1) by word-of-mouth throughout the Dutch Society of Shoulder and Elbow Surgery, and the Leiden University Nerve Centre, and (2) approaching two medical liability insurance companies. Medical records were reviewed to determine patient characteristics, disease history and postoperative course. Surgical records were reviewed to determine surgical details. RESULTS: A total of eight patients were collected, four men and four women, ageing 21-54 years. In five out of eight patients (62.5%), the ulnar nerve was affected; in the remaining three patients (37.5%), the radial nerve was involved. Possible causes for nerve injury varied among patients, such as portal placement and the use of motorized instruments. CONCLUSIONS: A case series on permanent nerve injury as a complication of elbow arthroscopy is presented. Reporting on this sequel in the literature is little, however, its risk is not to be underestimated. This study emphasizes that permanent nerve injury is a complication of elbow arthroscopy, concurrently increasing awareness and thereby possibly aiding to prevention. LEVEL OF EVIDENCE: IV, case series.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".