Genomic Data Heterogeneity across Molecular Diagnostic Laboratories
Bibliographic record
Abstract
Genomic data variability from laboratory reports can impact clinical decisions and population-level analyses; however, the extent of this variability and the impact on the data's value are not well characterized. This pilot study used anonymized genetic and genomic test reports from the Connect Myeloid Disease Registry (NCT01688011), a multicenter, prospective, observational cohort study of patients with newly diagnosed myelodysplastic syndromes, acute myeloid leukemia, or idiopathic cytopenia of undetermined significance, to analyze laboratory test variabilities and limitations. Results for 56 randomly selected patients enrolled in the Registry were independently extracted and evaluated (data cutoff, January 2020). Ninety-five reports describing 113 assay results from these 56 patients were analyzed for discrepancies. Almost all assay results [101 (89%)] identified the sequencing technology applied, and 94 (83%) described the test limitations; 95 (84%) described the limits of detection, but none described the limit of blank for detecting false positives. RNA transcript identifiers were not provided for 20 (43%) variants analyzed by next-generation sequencing and reported by the same laboratory. Of 42 variants with variant allele frequencies ≥30%, 16 (38%) of the variants did not have report text indicating that the variants might be germline. Variabilities and lack of standardization present challenges for incorporating this information into clinical care and render data collation ineffective and unreliable for large-scale use in centralized databases for therapeutic discovery.
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 | 0.099 | 0.238 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".