Circular statistics for analyzing changes in retinal nerve fiber layer
Bibliographic record
Abstract
OBJECTIVE: To evaluate the use of circular statistics to analyze retinal nerve fibre layer (RNFL) thickness in eyes with and without a prior history of optic neuritis (ON). DESIGN: Single-centre consecutive study. PARTICIPANTS: Twenty-two multiple sclerosis patients and 20 healthy control subjects. METHODS: Data on 28 eyes with a history of ON of 22 multiple sclerosis patients and 40 eyes of 20 healthy control subjects collected in 2010 and 2015. RNFL thickness was measured separately in 12 sectors around the optic nerve head. We used circular statistics to calculate the mean weighted vector of RNFL thickness for each sector and eye in 2 measurements made 5 years apart (2010 and 2015). Comparisons of weighted mean vectors between groups were made using a paired Mardia-Watson-Wheeler test. RESULTS: The directions of the mean weighted vectors for ON eyes were 45.8º in 2010 and 56.0º in 2015, whereas in control eyes the directions were 319.4º in 2010 and 188.9º in 2015. No significant differences were found between 2010 and 2015 in any of the 2 groups. However, significant differences were found between ON and control eyes in 2010 and 2015. CONCLUSIONS: This paper provides an example of how to use circular statistics in cases of directional data in ophthalmology and demonstrates that circular statistics are a suitable tool for this purpose.
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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 | 0.015 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".