Mistaken Diagnosis of Keratoconus Because of Corneal Warpage Induced by Hydrogel Lens Wear
Bibliographic record
Abstract
PURPOSE: We report a case of corneal warpage secondary to hydrogel lens wear that was initially mistaken as a case of keratoconus. METHODS: A 26-year-old Chinese female hydrogel lens wearer presented with an interest in refractive surgery. After topographies and pachymetries were performed, keratoconus was initially diagnosed for her right eye and suspect keratoconus diagnosed for her left eye. This conclusion was felt to be confirmed at a follow-up visit 1 week later, but keratoconus contact lens treatment was delayed because of the presence of superficial punctate keratitis. RESULTS: After 8 weeks without lens wear, corneal maps were performed again. The maps now showed regular with-the-rule astigmatism, and none of the previous evidence of keratoconus. Central pachymetries were also normal. CONCLUSIONS: Soft contact lens wear can induce corneal warpage mimicking keratoconus. Had the standard treatment for keratoconus been implemented before resolution of the warpage, it could have proven injurious to the patient, because the treatment itself could have provided an impetus for the protrusion to remain or perhaps even progress. Our case gives clinicians reason to pause when dealing with contact lens wearers presenting with corneal curvature irregularities such as keratoconus or ectasia, because of the possibility of lens-induced warpage.
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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 | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".