jamaneurology_morton_2022_rv_220001_1646244188.29201.pdf
Bibliographic record
Abstract
IMPORTANCE Infants with hypotonia can present with a variety of potentially severe clinical signs and symptoms and often require invasive testing and multiple procedures. The wide range of clinical presentations and potential etiologies leaves diagnosis and prognosis uncertain, underscoring the need for rapid elucidation of the underlying genetic cause of disease. OBSERVATIONS The clinical application of exome sequencing or genome sequencing has dramatically improved the timely yield of diagnostic testing for neonatal hypotonia, with diagnostic rates of greater than 50% in academic neonatal intensive care units (NICUs) across Australia, Canada, the UK, and the US, which compose the International Precision Child Health Partnership (IPCHiP). A total of 74% (17 of 23) of patients had a change in clinical care in response to genetic diagnosis, including 2 patients who received targeted therapy. This narrative review discusses the common causes of neonatal hypotonia, the relative benefits and limitations of available testing modalities used in NICUs, and hypotonia management recommendations. CONCLUSIONS AND RELEVANCE This narrative review summarizes the causes of neonatal hypotonia and the benefits of prompt genetic diagnosis, including improved prognostication and identification of targeted treatments which can improve the short-term and long-term outcomes. Institutional resources can vary among different NICUs; as a result, consideration should be given to rule out a small number of relatively unique conditions for which rapid targeted genetic testing is available. Nevertheless, the consensus recommendation is to use rapid genome or exome sequencing as a first-line testing option for NICU patients with unexplained hypotonia. As part of the IPCHiP, this diagnostic experience will be collected in a central database with the goal of advancing knowledge of neonatal hypotonia and improving evidence-based practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.661 | 0.362 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".