Whole Exome Sequencing in Idiopathic Short Stature: Rare Mutations Affecting Growth
Bibliographic record
Abstract
Abstract Introduction Evaluation of short stature is a challenge for pediatricians and in the process, idiopathic short stature (ISS) is an often diagnosis of exclusion. Non-pathogenetic mutations affecting height may present with phenotypes similar to the pathogenetic mutations. In this study, we aim to identify the underlying genetic cause of short stature in patients diagnosed with ISS and investigate potential treatments for them. Materials and Methods We identified 14 children in our practice who were under the age of 15 and were initially labelled as ISS. Then, we evaluated their plasma whole-exome sequencing (WES). Results Out of the 14 patients assessed with WES, five had normal results and correctly diagnosed with ISS. However, four of them had rare mutations that have not been extensively studied in the past. Due to the functions of these mutated genes and our patients’ phenotypes, we suspect that these mutations played a role in the short stature. Out of the remaining five patients, four had genetic mutations known to cause short stature and one had a mutation that was known not to affect height. Conclusion In patients who are initially diagnosed with ISS, WES can help to identify rare mutations that may play a role in short stature. Directing attention to these genes, may help with the correct diagnosis and choosing proper treatment for the patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".