Lessons learned from research on choroideremia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Having devoted over 35 years of my professional life to various projects on choroideremia (CHM), I began to reflect on the many lessons that I learned along the way. One of the most important is: we should pay careful attention to possible, unintended psychological harm in clinical research. This lesson was learned early and then reinforced when I engaged CHM patients in an investigator-sponsored Phase IB clinical trial of ocular gene therapy for choroideremia. My second lesson came from the trial itself in that preliminary data may not be sufficient to predict the risks to patients in a clinical trial. In the significant push to begin a gene therapy trial for CHM patients, writing grants, recruiting personnel, interacting with regulatory authorities, acquiring research equipment to test outcome measures, I missed a third lesson. There is significant bias when the principal investigator of an investigator-sponsored clinical trial is also the treating physician in the trial. Ideally, those two roles should be kept separate. Finally, having completed the clinical trial, I learned that gene replacement with an AAV vector may not be the only genetic therapy for CHM; an antisense oligonucleotide therapy may be possible in select cases.
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.
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) | 0.001 | 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 it