Choroideremia
Bibliographic record
Abstract
PURPOSE OF REVIEW: Although much has been written to define the phenotype and genotype of choroideremia (CHM), research continues to provide new insights that serve to better understand its pathogenesis and the directions for potential experimental therapies. RECENT FINDINGS: We would like to highlight new findings, expanding the type of disease-causing mutations to include mutations in the CHM promoter that will dramatically influence gene expression. Information derived from careful phenotyping of patients points increasingly to the central role of the retinal pigment epithelium as the key cell layer affected in the degenerative process. Finally, we will review the current initiatives that are testing vector-mediated gene replacement approaches in humans, including our current understanding of the likelihood of success by this approach. SUMMARY: Clinical and basic vision science have benefited greatly by the active engagement of patients with CHM in clinical research studies. The impetus for their involvement in these studies has been generated by the initial results of safety from subretinal injection of and AAV2.REP1 vector in humans. Follow-up studies in the next few years are expected to show if this approach will modify disease progression.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".