The tear ferning test: a simple clinical technique to evaluate the ocular tear film
Bibliographic record
Abstract
A healthy tear film is very important for many major functions of the ocular surface. Dry eye disease is a significant clinical problem that needs to be solved but the poor correlation between clinical signs and reported symptoms makes it difficult for the clinician to apply a scientific basis to his clinical management. The problem is compounded by the difficulties of evaluating the tear film due to its transparency, small volume and complex composition. Practical insight into tear film composition would be very useful to the clinician for patient diagnosis and treatment but detailed analysis is restricted to expensive, laboratory-based systems. There is a pressing need for a simple test. The tear ferning test is a laboratory test but it has the potential to be applied in the clinic setting to investigate the tear film in a simple way. Drying a small sample of tear fluid onto a clean, glass microscope slide produces a characteristic crystallisation pattern, described as a 'tear fern'. This test is currently not widely used because of some limitations that need to be overcome but several studies have demonstrated its potential. Such limitations need to be resolved so that tear ferning could be used in the clinic setting to assess the tear film.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".