Response to the Letters Regarding the North American Society of Pediatric Gastroenterology, Hepatology and Nutrition NAFLD Guidelines
Bibliographic record
Abstract
Reply: We thank the authors for their interest in the recent North American Society of Pediatric Gastroenterology, Hepatology and Nutrition (NASPGHAN) guidelines on pediatric nonalcoholic fatty liver disease (NAFLD) (1). Dr Vajro calls for a screening approach to minimize the rate of false negatives so that children with NAFLD will not be missed. The suggestions include the use of norm-based aminotransferase cutoffs with additional consideration to the use of abdominal ultrasound, as is recommended in the European Society of Pediatric Gastroenterology, Hepatology and Nutrition (ESPGHAN) guidelines. In direct contrast, Drs Koot and Benninga call for setting the ALT cutoff higher to improve specificity while decreasing sensitivity with the thought that, “missing NAFLD is not directly harmful.” Finally, Dr Noritz points out the differential diagnosis is even broader than what was included in the guideline, and suggests inclusion of creatine kinase in the diagnostic evaluation to assess for occult muscle disease as a cause of aminotransferase elevation. Collectively these letters support the complexity of screening for NAFLD and the challenge of identifying the cause of elevated liver chemistry once it is detected. In the guidelines, ALT is recommended as a screening tool because it is universally available, inexpensive, and has the largest body of evidence to supports its use. We agree that is important to use the correct cutoffs in interpreting ALT. The data presented are based upon the SAFETY study from the United States and the CALIPER study from Canada (2,3). Taken together, the aminotransferase cutoffs suggested in these studies were derived from populations that reflect nearly two-thirds of the North American population. The combination of high cost of ultrasound combined with low diagnostic accuracy limit its utility as a screening tool (4). ALT also has the advantage of being more amenable to repeat measurement and having a stronger relationship with disease severity. As also noted in the guidelines, having a one-time determination of any degree of ALT elevation is not sufficient to make a diagnosis of NAFLD, or to know the cause of the elevation. Nevertheless, we acknowledge the ongoing challenges of deciding when to repeat liver chemistries, the extent of additional testing required, and at what point to consider liver biopsy. In addition to the evidence-based recommendations, the guidelines document offered an algorithm that balances the need for clinical judgment regarding an individual patient with some guidance on how to interpret and evaluate ALT elevation. This algorithm uses the degree of ALT elevation to help direct the clinical approach. The document is intended to guide the clinician, not rigidly dictate an approach nor remove clinical judgment. The clinical history may support testing for etiologies beyond those enumerated in the guideline, such as creatine kinase. The ALT cutoffs represented in the algorithm are derived from the relevant setting of a large study of screening done in primary care with referral to pediatric gastroenterology for evaluation (5). Notably, 11% of children in this study who were overweight or obese with an ALT elevation were found to have advanced fibrosis. Thus, we agree that identifying NAFLD is important and would like to underscore that the concept that NAFLD is not harmful to children is outdated. There is a growing body of literature showing the risk for serious hepatic, endocrine, and cardiovascular outcomes in children with NAFLD (6–9).
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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 | 0.005 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.032 | 0.037 |
| Insufficient payload (model declined to judge) | 0.017 | 0.017 |
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".