Increasing incidence of anterior cruciate ligament reconstruction: a 17‐year population‐based study
Bibliographic record
Abstract
PURPOSE: Anterior cruciate ligament (ACL) injuries are one of the most frequently studied injuries in orthopedic care and research. However, limited epidemiological data are available in Canada regarding trend and distribution of anterior cruciate ligament reconstruction (ACLR). In this paper, our purpose was to assess trends of ACLR between 2002/03 and 2018/19 by age, sex, season of surgery, and location (inpatient vs outpatient) of surgery. METHODS: In this descriptive epidemiological study of retrospective data available from Alberta Ministry of Health, we report annual incidence of ACLR between 2002/03 until 2018/19 among Albertans aged 10 years and older. Information was collected by authors from physician claims database for primary ACLR and revision ACLR and linked with other databases. Incidence proportions (number of ACLR/100,000 population) were calculated and compared by age category and gender over the study period. RESULTS: A total of 28,401 primary ACLR and 2085 revision ACLR were identified during the study period. Age-standardized annual incidence of primary ACLR increased from 40.6 to 51.2 per 100,000 population aged 10 years and older. Average annual increase in ACLR incidence was higher among females (1.8% per years) compared to males (0.96% per year). The overall peak incidence and peak incidence among males was observed in 20-29 year age group, whereas peak incidence in females was observed in 10-19 years of age. The number of ACLR in females outnumbers those among males for 10-19 year age group. Generally, a lower proportion of ACLR were conducted in summer compared to other seasons. Primary ACLR conducted in outpatient setting increased from 72% in 2002/03 to 97% in 2018/19. CONCLUSION: The incidence of ACLR is increasing in Alberta, especially among females and among younger cohorts under 20 years of age. This information can help clinicians to provide patient education and policy-makers to design and implement targeted ACL injury prevention programs. LEVEL OF EVIDENCE: Level III.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".