Retinal thickness measurements in sickle cell patients with HbSS and HbSC genotype
Bibliographic record
Abstract
OBJECTIVE: Temporal macula thinning has been reported in sickle cell patients, but it remains unclear if there is a difference between HbSS and HbSC genotypes. We aimed to quantitatively compare macular thickness between eyes with HbSS and HbSC genotype. DESIGN: Retrospective descriptive study. METHODS: Consecutive patients seen over a 5.5-year period in the Ophthalmology Department at St Thomas' Hospital, London, were identified. Macular optical coherence tomography images were retrospectively analyzed. The retinal thickness in all 9 subfields of the Early Treatment Diabetic Retinopathy Study (ETDRS) grid was compared between HbSS and HbSC eyes. Right eyes and left eyes were analyzed independently, as well as averaged measurements from both eyes. Comparison was made between the 2 genotypes, adjusting for age and sex, and for multiple testing. Scans were excluded in cases of poor fixation or ocular comorbidity affecting retinal thickness. RESULTS: 132 HbSC and 120 HbSS patients were identified. Scans from 166 right and 153 left eyes were included (with approximately equal numbers of HbSS and HbSC genotypes). Mean retinal thickness was lower in HbSS eyes compared with HbSC eyes in all subfields of the ETDRS grid, but in most subfields the difference was <10 microns. Differences reached statistical significance for outer superior, inferior, and temporal subfields and the inner temporal subfield (p < 0.05). CONCLUSION: Although the HbSC genotype is more strongly associated with proliferative retinopathy, HbSS patients had on average more macular thinning.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".