OCT and OCTA in dysthyroid optic neuropathy: a systematic review and meta-analysis
Bibliographic record
Abstract
PURPOSE: To explore the current research about the role of optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) in dysthyroid optic neuropathy (DON). METHODS: Studies in the literature that focused on OCT, OCTA and DON were retrieved by searching PubMed, EMBASE, Cochrane databases and Clinical Trial before 20 June 2023. The methodological quality was assessed using the Newcastle-Ottawa scale. The quantitative calculation was performed using Review Manager V.5.3. RESULTS: Twelve studies met the eligibility criteria and were included. DON group presented lower macular ganglion cell complex in the overall, superior and inferior hemifields compared with the non-DON group. Furthermore, the ganglion cell layer and inner plexiform layer in DON group was thinner in contrast to the non-DON group. The optic nerve head vessel density was lower in the DON group than that in the non-DON group. A reduction of radial peripapillary capillary vessel density could be seen in the DON group than the non-DON group in overall, inside disc, peripapillary, superior-hemifield, temporal and nasal. Besides, the macular superficial retinal capillary layer of non-DON and DON is lower than the healthy control group. CONCLUSIONS: This study supported the potential value of OCT and OCTA metrics as novel biomarkers of DON. Ophthalmologists should comprehensively consider the retinal structure and microvasculature in dealing with DON. ETHICS AND DISSEMINATION: This systematic review included data from published literature and was exempt from ethics approval. Results would be disseminated through peer-reviewed publication and presented at academic conferences engaging clinicians. PROSPERO REGISTRATION NUMBER: CRD42023414907.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.017 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".