Retinal nerve fiber layer changes in migraine
Bibliographic record
Abstract
BACKGROUND: Migraine is a common neurological disease, which seriously affects the quality of life and daily activities of patients. Although migraine is a transient phenomenon of cerebral vasoconstriction, it is well documented that recurrent attacks of migraine may lead to abnormalities in retinal structure. Optical coherence tomography (OCT) is a sensitive method to detect subtle damage in retinal nerve fiber layer (RNFL). There have been many studies investigating the difference in RNFL thickness with optical coherence tomography (OCT) between migraine patients and healthy controls. However, the results were not consistent. Our purpose is to perform a meta-analysis to investigate RNFL alterations in migraine. METHODS: We will search PubMed, Embase, Web of science for studies assessing the differences in RNFL measured by OCT between patients with migraine and healthy controls. Case-control studies published in English will be included. Two reviewers will independently screen eligible articles, extract data, and assess quality. This meta-analysis will synthesize selected research data and compare the difference in RNFL thickness between patients with migraine and healthy controls. We will use Stata 15 in this meta-analysis. I statistics will be used to assess heterogeneity. If I ≤ 50%, the data are synthesized will use a fixed effect model. Otherwise, a random effect model will be performed. Publication bias will be determined by the Egger test. The methodological quality of all included studies will be evaluated by the Newcastle-Ottawa Scale (NOS). We will perform subgroup analysis, sensitivity analysis, and meta-regression analysis to test the robustness of the results. RESULTS: We will obtain quantitative results regarding the difference in RNFL thickness between migraine patients and healthy controls. The results will be published in a peer-reviewed journal. CONCLUSIONS: The results of this study provide a high-quality synthesis of existing evidence and provide a basis for assessing the effect of migraine on the thickness of RNFL. REGISTRATION NUMBER: INPLASY 202060033.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".