Bibliographic record
Abstract
I would counsel strongly against a “genetic test” for this young girl, even if there were a sensitive and specific genetic test available to determine the genetic status of an individual from this family. I would similarly argue that it would be wrong to perform such a test even if there were a sensitive and specific genetic test available to determine the genetic status of an individual from this family. The problem we face in managing this family is how best to present this view to parents who, as is not unusual, have already decided that a DNA test is exactly what their daughter needs. Let‘s imagine for a moment that we have the technology and detective skills to offer genetic testing for this girl. A positive result means she has a 100% chance of carrying the gene causative for RP in the family, and a negative result that there is a 100% chance that she has escaped inheriting the gene causative for RP in this family. This is rarely the case given the vast genetic heterogeneity of this disease. How might this unusual state of affairs have arisen? Well, we might have found that the family is segregating a missense mutation in the rod opsin gene, which would be the first gene investigated in a laboratory for the very reason that it is the most common cause of autosomal dominant RP.1 Rarely are we able to determine the mode of inheritance in a patient presenting with RP, but in this family, given the male to male transmission and three affected individuals, autosomal dominant (AD) RP seems to be on the cards. So, we have previously approached the family members, both affected and unaffected, expounding on the great future benefits of molecular research into these conditions, though we do …
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads 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".