INTRAOPERATIVE IMAGING TO DETECT OCCULT PENETRATION OF SCREWS AFTER VOLAR PLATING OF DISTAL RADIUS FRACTURES: A CADAVERIC STUDY
Bibliographic record
Abstract
Hardware prominence is one of the major established complications following volar plating of distal radius fractures. The purpose of this cadaveric study is to compare two conventional fluoroscopic imaging views (Carpal Shoot-Through [CST] and Dorsal Horizon [DH] views) with ultrasound to establish the best intra-operative imaging modality for surgeons to use to identify penetration of screws through the dorsal cortex and / or into the distal radioulnar joint. Twelve human cadaveric upper limb specimens were instrumented with distal radius variable angle locking plates and four distal locking screws from a volar approach. CST, DH views and ultrasound evaluations were compared to identify prominent screws. There were 6 surgeons divided into 3 groups of different clinical experience performing the evaluations. Sensitivity and specificity of detecting screw penetration along with the surgeon's confidence in each modality were established. CST view was the most sensitive in identifying the presence of dorsal screw penetration (100%) and absence of dorsal screw penetration (78%). DH view had the highest sensitivity in recognizing DRUJ screw penetration (89%). Ultrasound evaluation had the lowest sensitivity and specificity (28%, 56% respectively). The fellowship trained upper extremity surgeons had the highest sensitivity and specificity rate of 78% and 78% respectively. Surgeon's ability to perform as well as confidence in evaluating for screw penetration was highest with the CST view. CST view was found to be the best intra-operative imaging modality to “rule in” and “rule out” screw penetration through the dorsal cortex. DH view was most reliable in detecting DRUJ screw penetration. Clinical experience was determined to be an important factor for both dorsal cortex and DRUJ evaluation. Ultrasound evaluation had the lowest sensitivity and specificity in all categories, demonstrating that this not a reliable modality for surgeons without specific training in point-of-care ultrasonography.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".