The use of DXA for early detection of pediatric cystic fibrosis‐related bone disease
Bibliographic record
Abstract
BACKGROUND: Cystic fibrosis (CF)-related bone disease (CFBD) is seen in adults and can be associated with respiratory illness and malnutrition. There is limited and conflicting data regarding CFBD in pediatric CF. With longer life expectancy and promotion of disease prevention, pediatric CFBD demands further investigation. METHODS: Our center initiated a quality improvement (QI) project from April 2016 to December 2018 to improve CFBD screening in patients 8 years or older, per current CF Foundation (CFF) guidelines. Our team formulated a dual-energy X-ray absorptiometry (DXA) scan algorithm based upon degree of bone mineral density (BMD); shared CFBD guideline recommendations in our quarterly newsletter; and ordered scans for eligible patients at weekly review meetings. We reviewed DXA results from 141 patients after institutional review board approval and gathered data including comorbidities, genetics, anthropometric measures, medication exposure, and relevant serum studies. RESULTS: %) (p < 0.001) as well as lower body mass index % (p = 0.001). Patients with lower BMD were overall older at time of DXA (p = 0.016). During study duration, 13 patients who had abnormal DXA results underwent repeat DXAs after physical therapy; 11 of the 13 showed improvement in DXA results. CONCLUSIONS: A DXA scan is a useful screening tool and can be used to identify pediatric patients who could benefit from further therapy and interventions to preserve adequate bone health and avoid further loss. QI initiatives can lead to improved screening and diagnosis and earlier intervention such as physical therapy. Further studies are needed to better understand the utility of physical therapy in children with CF.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".