Epidemiology and Natural History of Acute Patellar Dislocation
Bibliographic record
Abstract
BACKGROUND: The goals of this study were to (1) define the epidemiology of acute patellar dislocation, (2) determine the risk of subsequent patellar instability episodes (subluxation and/or redislocation) during the study period, and (3) identify risk factors for subsequent instability episodes. STUDY DESIGN: Prospective cohort study. METHODS: The authors prospectively followed 189 patients for a period of 2 to 5 years. Historical data, injury mechanisms, and physical and radiographic measurements were recorded to identify potential risk factors for poor outcomes. RESULTS: Risk was highest among females 10 to 17 years old. Patients presenting with a prior history of instability were more likely to be female (P < .05) and were older than first-time dislocation patients (P < .05). Fewer first-time dislocators (17%) had episodes of instability during follow-up than patients with a previous history of instability (49%) (P < .01). After adjusting for demographics, patients with a prior history had 7 times higher odds of subsequent instability episodes during follow-up than first time dislocators (adjusted odds ratio = 6.6, P < .001). CONCLUSIONS: Patellar dislocators who present with a history of patellofemoral instability are more likely to be female, are older, and have greater risk of subsequent patellar instability episodes than first-time patellar dislocators. Risk of recurrent patellar instability episodes in either knee is much higher in this group than in first-time dislocators.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".