Perceived vs. true glenoid anchor placement: a cadaveric comparison of the beach chair and lateral position
Bibliographic record
Abstract
Purpose To explore whether patient position influences a surgeon's ability to accurately judge anchor position on the glenoid. Materials and Methods Two anchors were inserted into the glenoid of 8 shoulders. Arthroscopic videos were taken from 3 views (posterior beach chair [pBC], posterior lateral decubitus [pLD], and anterosuperolateral decubitus [asLD]). The shoulders were disarticulated to identify "true" anchor position. Seventeen shoulder surgeons reviewed the videos and indicated anchor positions using the "clock face" method. Accuracy was measured within tolerances, ranging from zero (exact), 0.5 (half-hour), 1.0, and 1.5 hours of "true" position. Intra- and inter-rater agreement was calculated. Post hoc analyses explored for bias dependent on surgical side. Results The overall accuracy was 34.0%. At tolerances of 0.5, 1.0, and 1.5 hours, accuracy increased to 82.4%, 95.4%, and 98.0%. With a 30° scope, identification of exact position was more accurate in pBC than pLD (odds ratio [OR] = 1.397; P = .029) but not asLD (OR =1.341; P = .197). At a tolerance of 0.5 hour, the 30° scope was more accurate in pBC than both pLD (OR = 1.444; P = .011) and asLD (OR = 1.728; P = .009). In left shoulders, anchors were perceived as more inferior than true position in asLD and pLD. In right shoulders, anchors were perceived as more superior than true position from pBC and pLD. Inter- and intrarater agreement were highest in pBC with a 30° scope (30° scope weighted kappa=0.783 and 70° scope weighted kappa=0.853, respectively). Conclusion Judgment of anchor position on video is most accurate in a pBC view. Inter- and intrarater reliability were also highest from a pBC view.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".