ACUTE ULNAR LENGTHENING FOR FOREARM DEFORMITY IN HEREDITARY MULTIPLE EXOSTOSES
Bibliographic record
Abstract
To evaluate effectiveness and safety of acute ulnar lengthening osteotomy in Madelung9s deformity associated with Hereditary Multiple Exostoses (HME) Seventeen ulnas in 13 patients had acute ulnar lengthening for HME associated forearm deformity. Defined radiographic parameters were compared pre- and post-operatively using student9s t-test; ulnar variance, carpal slip, radial bowing, radial articular angle. All complications were noted. Mean follow-up was 27 months (range 1.5 – 72months). An increase in ulna length by a mean of 15.4mm (range 4.5 – 29.3mm) was achieved acutely, corresponding to an increase of 9.3% of total ulnar length. Negative ulnar variance was improved from a pre-operative mean of 12.4mm (range 6.1–16.5mm) to a post-operative mean of 4.6mm (range 0–11.25mm) (p= Three patients required second procedures for recurrence of deformity at 18 months – 6 years following their primary operation. There were no incidences of compartment syndrome, neurovascular injury nor infection. One ulna fractured intra-operatively requiring a longer plate. One patient had a non union which united on revision surgery. Significant radiographic improvements in forearm and wrist alignment were seen with acute ulnar lengthening. The procedure is safe, with no compartment syndrome nor neurovascular injury and low complications rate. Recurrence rates in the skeletally immature patients are comparable to that reported with gradual lengthening. Acute ulnar lengthening for forearm deformity associated with HME has been demonstrated to be a safe, reproducible and effective surgical procedure.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".