Diagnostic significance of neutrophil-to-lymphocyte ratio in non-arteritic anterior ischemic optic neuropathy: a meta-analysis
Bibliographic record
Abstract
BACKGROUND: We aimed to determine the association of neutrophil-to-lymphocyte ratio (NLR) with non-arteritic anterior ischemic optic neuropathy (NAION). METHODS: We conducted a systematic review and meta-analysis according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. PubMed, Scopus and Web of Science were searched from the establishment of the database to May 5, 2022 to find the relevant studies. The quality of the included literature was evaluated with the Newcastle-Ottawa scale (NOS). The results are reflected in the form of standard mean difference (SMD) and 95% confidence interval (CI). RESULTS: = 0.0%, p = 0.60); thus, the analysis used the fixed-effect model. The pooled sensitivity of NLR was 0.69 (95% CI 0.60-0.67), and the pooled specificity was 0.59 (95% CI 0.50-0.67). The pooled positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio (DOR) of NLR were 1.71(95%CI 1.48-1.98), 0.50 (95%CI 0.41-0.62), and 3.38 (95%CI 2.57-4.44), respectively. CONCLUSIONS: Our findings suggest NLR to be a potential marker of NAION, while also implicating a role for inflammation in underlying pathophysiology.
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 | 0.019 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.053 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".