Awareness, management, and practice patterns of pediatric NAFLD by primary care physicians
Bibliographic record
Abstract
Background: Nonalcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease in children. Primary-care physicians (PCPs) play a key role in identifying patients requiring specialist referral. In this study, we aim to determine PCPs' practice patterns for paediatric NAFLD, as knowledge gaps have been reported for adult NAFLD. Methods: A survey was sent to 60 PCPs in the Eastern Ontario Network from July 2019 to January 2020. Results: Thirty-seven (62%) PCPs responded to the survey. Twenty-one incorrectly considered the prevalence of paediatric NAFLD to be ≤10%. The majority (35/36) cared for less than five paediatric NAFLD patients. Thirty-four (92%) were only 'slightly familiar' or 'not familiar at all' with paediatric NAFLD. Only one PCP routinely screens for NAFLD. Only one PCP was aware of the North American Society for Pediatric Gastroenterology, Hepatology, and Nutrition (NASPGHAN) clinical guidelines for paediatric NAFLD. Twenty-five (68%) correctly selected lifestyle modifications as a treatment option. Lack of confidence in the knowledge of NAFLD was the most common barrier for managing paediatric cases. Conclusion: The majority of PCPs are not screening for paediatric NAFLD and are not familiar with its clinical spectrum, citing a lack of knowledge regarding NAFLD as the greatest barrier. This may cause delays in diagnosis and a presentation with advanced fibrosis at the time of specialist referral. Dissemination and implementation of clinical guidelines have the potential to improve knowledge and screening rates for NAFLD in children at the primary-care level.
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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 | 0.001 | 0.008 |
| 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 | 0.001 | 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".