Relative importance of tear homeostatic signs for the diagnosis of dry eye disease
Bibliographic record
Abstract
AIM: Disease misdiagnosis is more likely if standardised diagnostic criteria are not used. This study systematically examined the effect on diagnosing dry eye disease (DED), when tests for evaluating tear film homeostasis were included or excluded from a multi-test protocol. METHOD: For 1,427 participants across five sites, data for the full suite of diagnostic tests defined in the Tear Film and Ocular Surface Society Dry Eye Workshop II (TFOS DEWS II) Diagnostic Methodology report algorithm were evaluated; diagnostic sensitivity was calculated when individual signs were removed, and when different combinations of signs were required. RESULTS: Evaluating just one of the three TFOS DEWS II homeostatic signs resulted in between 12.3 % and 36.2 % of patients who met the DED diagnostic criteria not being assigned this diagnosis. While comprehensive ocular surface staining evaluation, comprising of corneal, conjunctival and lid margin staining, in combination with symptoms had the highest sensitivity (87.7 %) of the three markers, the sensitivity dropped to 44.6 % if only corneal staining was evaluated. Omitting either non-invasive tear breakup time or tear osmolarity each dropped the sensitivity by <5 %. The prevalence of DED was substantially reduced if a diagnosis required symptoms and two of the three signs to be present (by 43.7 %-61.2 %) and by 65.9 % if all three signs indicating a loss of tear film homeostasis were required. The outcomes of the analysis did not change significantly across differing severities of DED symptoms. CONCLUSIONS: The TFOS DEWS II diagnostic algorithm of symptoms plus assessing for a tear film (non-invasive tear breakup time or tear osmolarity) and ocular surface sign can be considered a robust and appropriate approach for DED diagnosis.
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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 | 0.061 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".