Incidence of Total Knee Replacement in Patients With Previous Anterior Cruciate Ligament Reconstruction
Bibliographic record
Abstract
OBJECTIVE: To determine the rate of total knee replacement (TKR) after anterior cruciate ligament reconstruction (ACL-R) compared to the general population. DESIGN: Retrospective review. SETTING: All hospitals that performed TKR and ACL-R in Manitoba between 1980 and 2015. PARTICIPANT: All patients that underwent TKR and ACL-R in Manitoba between 1980 and 2015. INTERVENTION: Patient factors gathered at time of surgery included: age, sex, urban or rural residence, neighborhood income quintile, and resource utilization band (RUB). Each person was matched with up to 5 people from the general population who had never had ACL-R and had not had a TKR at the time of the case ACL-R. MAIN OUTCOME MEASURES: The rate of TKR after ACL-R. RESULTS: Overall from 1980 to 2015, 8500 ACL-R were identified within the 16 to 60 years age group with a resultant 42 497 population matches. Sex was predominantly male. The mean age of the ACL-R group at the time of TKR was 53.7 years, whereas the mean age for the matched cohort was 58.2 years, P < 0.001. Those with ACL-R were 4.85 times more likely to go on to have TKR. Apart from age, no other risk factors examined (location, year of surgery, place of residence, income quintile, and RUB) seemed to increase risk of TKR after ACL-R. CONCLUSION: Patients who underwent ACL-R were 5 times more likely to undergo TKR.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 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".