Clinical Outcomes of Primary versus Revision Surgery using Arthroscopic Anatomic Glenoid Reconstruction for Anterior Shoulder Instability. (244)
Bibliographic record
Abstract
Objectives: Revision surgeries after prior shoulder stabilization are known to have worse outcomes as compared to their primary counterparts. To date, no studies have looked at the utility of arthroscopic anatomic glenoid reconstruction (AAGR) as a revision surgery. The purpose of this study was to assess the clinical outcomes of primary versus revision AAGR for anterior shoulder instability with bone loss. Methods: We performed a retrospective review on consecutive patients with prospectively collected data who underwent AAGR from 2012 to 2018. Patients who received AAGR for anterior shoulder instability with bone loss and had a minimum follow-up of two years were included. Exclusion criteria included patients with rotator cuff pathology, multidirectional instability and glenoid fractures. There were 68 patients (48 primary and 20 revision) who met inclusion/exclusion criteria. Our primary outcome was measured using the Western Ontario Shoulder Instability Index (WOSI) and Disabilities of Arm, Shoulder, Hand (DASH) scores. Secondary outcomes included post-operative complications and post-operative recurrent instability. Results: The primary group showed a significant improvement in most-recent post-operative WOSI from 62.7 to 20.7 (P<0.001, α=0.05) and in DASH from 26.89 to 6.7 (p<0.001, α=0.05). The revision group also showed a significant improvement in WOSI from 71.5 to 34.6 (p<0.001, α=0.05) and in DASH from 39.5 to 17.0 (p<0.05, α=0.05). When comparing between groups, the revision group had worse WOSI scores (34.6) at most recent follow-up compared to the primary group (20.7); p<0.05. The most-recent DASH scores also showed the revision group (17.0) having worse outcomes than the primary group (6.7); p<0.05. Important to note that the minimal clinically important difference (MCID) was met for WOSI (MCID=10.4) but not DASH (MCID=10.83). There were no post-operative reports of instability in either group. For complications, one hardware failure (suture anchor) was seen in the primary group, and two hardware removals were seen in the revision group. Conclusions: While patient reported scores indicated worse outcomes in the revision group, the significant clinical improvement in DASH and WOSI, along with the lack of recurrent instability provides evidence that AAGR is a suitable option for revision patients.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".