Spectral Retrieval of Latent Heating Profiles from TRMM PR Data: Comparison of Look-Up Tables
Bibliographic record
Abstract
Whole-genome sequencing (WGS) as a first-tier diagnostic test could transform medical genetic assessments, but there are limited data regarding its clinical use. We previously showed that WGS could feasibly be deployed as a single molecular test capable of a higher diagnostic rate than current practices, in a prospectively recruited cohort of 100 children meeting criteria for chromosomal microarray analysis. In this study, we report on the added diagnostic yield with re-annotation and reanalysis of these WGS data ~2 years later. Explanatory variants have been discovered in seven (10.9%) of 64 previously undiagnosed cases, in emerging disease genes like HMGA2. No new genetic diagnoses were made by any other method in the interval period as part of ongoing clinical care. The results increase the cumulative diagnostic yield of WGS in the study cohort to 41%. This represents a greater than 5-fold increase over the chromosomal microarrays, and a greater than 3-fold increase over all the clinical genetic testing ordered in practice. These findings highlight periodic reanalysis as yet another advantage of genomic sequencing in heterogeneous disorders. We recommend reanalysis of an individual's genome-wide sequencing data every 1-2 years until diagnosis, or sooner if their phenotype evolves.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".