Significant Changes in the Diagnosis, Injury Severity and Treatment for Anterior Shoulder Instability Over Time in a U.S. Population
Bibliographic record
Abstract
Purpose To report the annual incidence of anterior shoulder instability (ASI) diagnosis, injury severity, and surgical stabilization in a U.S. population. Methods An established U.S. geographic database was used to identify patients < 40 years old with diagnoses of ASI from 1994‐2016. Medical records were reviewed to obtain patient demographics, histories, imaging results, and surgical details. Age‐ and sex‐specific incidence rates were calculated and adjusted to the 2010 U.S. population. Poisson regression was performed to examine trends by timeline, sex and age. Results The study population consisted of 652 patients with ASI and a mean age of 21.5 years (range, 3.6‐39.5). Comparing 2015‐2016 to 1994‐1999, we found an increase in the number of dislocations (from 1.0‐1.9; P = 0.016) and total instability events (from 2.3‐3.4; P = 0.041) per patient prior to presentation to a physician. There was a trend in increased diagnosis of bony Bankart and/or Hill‐Sachs on MRI over time, with these lesions documented in 96% of patients undergoing MRI in 2015‐2018 compared to 52.9% in 1994‐1999 ( P < .001). The use of arthroscopic procedures increased and peaked in 2005‐2009 (90% of surgical cases performed). The proportion of open Latarjet procedures increased from 2010‐2014 (14%) and 2015‐2018 (31%). Conclusions The age‐ and sex‐ adjusted incidence of ASI diagnosis in a U.S. population from 1994‐2016 is comparable to that demonstrated in Canadian and European populations. This study demonstrates an increasing number of instability events prior to surgical evaluation, which may correlate with patients’ more commonly presenting with bone loss and requiring more aggressive surgical treatment or that ASI is being more frequently cared for and documented by present‐day orthopedic surgeons. Level of Evidence Level III, cross‐sectional study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".