A case report of recurrent annular ligament displacement in a pediatric patient
Bibliographic record
Abstract
BACKGROUND: Annular ligament displacement (ALD) is a common pediatric condition whereby the annular ligament slips over the radius and remains trapped between the radial head and capitellum. It can be reduced with relative ease by performing passive pronation or supination with concomitant pressure over the radial head. However, ALD is more likely to recur after the initial incident, concerning for both the patient and caregivers. PURPOSE: This article describes the successful conservative management of a 2 year, 4 month old female patient with recurrent ALD, having occurred six times in an 8 month period. METHODS: The role of an orthosis, targeted exercises, and education for caregivers for treating recurrent ALD is discussed. RESULTS: At each follow up and final 18 month follow up no ALD events had occurred. CONCLUSIONS: Previous literature discussing treatment for recurrent ALD has examined teaching caregivers to reduce the elbow at home or teaching reductions via telehealth. However, there is a dearth of research around treating the issue so recurrence does not occur. This conservative intervention demonstrates a potential avenue for treating chronic recurrent ALD to eliminate subsequent events.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".