PEDIATRIC PATIENTS WITH FAI AND INFLAMMATORY COMORBIDITIES HAVE A CHARACTERISTIC CAM DEFORMITY: A PILOT STUDY
Bibliographic record
Abstract
Background: Pediatric patients with inflammatory conditions often present with single or multifocal joint pain. Assessment of these patients at our institution revealed a possible association between femoroacetabular impingement (FAI) and inflammatory comorbidities. The purpose of this study was to investigate the prevalence and unique characteristic morphology of FAI in pediatric patients with inflammatory comorbidities. Methodology: A retrospective review was conducted on a consecutive series of 105 patients diagnosed with FAI in our institution’s adolescent hip clinic from January 2016 to March 2018. Patients with an inflammatory comorbidity were identified. Femoral head-neck junction morphology was assessed qualitatively and with standard radiographic measures for FAI. Results: Seven patients (6.7% of diagnosed patients), with eight symptomatic hips, were found to have FAI and an inflammatory comorbidity. These diagnoses included Crohn’s disease, Celiac disease, juvenile idiopathic arthritis, Type 1 diabetes mellitus and hypothyroidism. All seven patients had cam deformities. Qualitative assessment revealed that most patients had a characteristic head-neck morphology that was unique to this subset of patients, with a prominent and “sharp-edged” bump at the head-neck junction (See Figure 1). Radiographic analysis of the symptomatic hips revealed a mean alpha angle of 81.3o (±8.7 o), mean LCEA of 33.1o (±8.4 o) and mean femoral offset of 0.07 (±0.05). There was no significant difference in the radiographic measurements between symptomatic and asymptomatic hips in these patients. Conclusion: This is the first study to identify an association between FAI and inflammatory comorbidities in pediatric patients, with a prevalence of 6.7% at our institution. Given the relative rarity of inflammatory comorbidities in the general pediatric patient, this represents an unexpectedly high association. These patients presented with a unique head-neck morphology characterized by a more prominent and “sharp-edged” cam lesion than the typical morphology seen in patients without an inflammatory comorbidity. This study raises questions regarding the possible role of physeal inflammation in the development of cam deformities and indicates a need for further studies investigating the relationship between systemic inflammatory disorders and FAI. [Figure: see text]
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".