Epidemiological Trends of Anterior Cruciate Ligament Reconstruction in a Canadian Province
Bibliographic record
Abstract
OBJECTIVE: To determine epidemiological trends of anterior cruciate ligament reconstruction (ACL-R) in a Canadian province, estimate the national incidence, and compare with internationally published data. DESIGN: Retrospective review. SETTING: All hospitals that performed ACL reconstructions in Manitoba between 1980 and 2015. PARTICIPANT: All patients that underwent ACL-R in Manitoba between 1980 and 2015. INTERVENTION: This is a retrospective review looking at deidentified, individual-level administrative records of health services used for the entire population of Manitoba (approximately 1.3 million). Codes for ACL and cruciate ligament reconstruction were searched from 1980 to 2015. Patient demographics included age, sex, geographic area of residence, and neighborhood income quintile. MAIN OUTCOME MEASURES: Trends of ACL reconstructions from 1980 to 2015. RESULTS: A total of 10 114 ACL-R were performed during the 36-year study period and patients were predominantly male (63.1%). The mean age at ACL-R was 29.5 years (SD 10.0) for males and 28.5 years (SD 11.9) for females, whereas age younger than 40 years accounted for 81.7% of all ACL-R. The incidence of ACL-R increased from 7.56/100 000 inhabitants in 1980 to 48.45/100 000 in 2015. The proportion of females undergoing ACL-R has increased from 29.3% in 1980% to 41.9% in 2015, and female patients now comprise the majority of ACL-R in the under-20 age category. CONCLUSION: The incidence of ACL-R has significantly increased since 1980; female patients now make up a greater proportion than males of the ACL-R population younger than 20 years. This information can be used to guide resource allocation planning and focus injury prevention initiatives.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".