The clinical implementation of whole genome sequencing: a conversation with seven scientific experts
Bibliographic record
Abstract
This commentary is based on the key note address given by Jordan Lerner-Ellis at the annual symposium of the Society for the Study of Inborn Errors of Metabolism, held in Geneva, Switzerland in August 2011. The content of the address was developed from a series of discussions with seven clinicians and medical geneticists, all having a long-time interest in genetic testing and genomic medicine (the interviews were not intended to be a forum for the introduction of new data). All participants were asked to offer their views on five questions: the benefits of using whole genome sequencing (WGS) in the clinic; the corresponding risks; the limitations on its wider use; the interviewees' particular interests in using WGS in their practice; and projected timelines for successful adoption in clinical medicine.
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 | 0.149 | 0.229 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.027 | 0.023 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.043 | 0.069 |
| 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".