Multimodal imaging documentation of rapid evolution of retinal changes in handheld laser-induced maculopathy
Bibliographic record
Abstract
PURPOSE: To use multimodal imaging to document the relatively rapid clinical evolution of handheld laser-induced maculopathy (HLIM). To demonstrate that inadvertent ocular injury can result from devices mislabeled with respect to their power specifications. METHODS: The clinical course of a 17-year-old male who sustained self-inflicted, central macular damage from a 20-25 s direct stare at a red-spectrum, handheld laser pointer ordered from an internet retailer is provided. Retrospective review of multimodal imaging that includes fundus photography, fluorescein angiography, MultiColor reflectance, eye-tracked spectral domain optical coherence tomography (SD-OCT), fundus autofluorescence, and microperimetry is used to describe the evolving clinical manifestations of HLIM in the first 3 months. RESULTS: Curvilinear bands of dense hyperreflectivity extending from the outer retina and following the Henle fibers were seen on SD-OCT immediately after injury. This characteristic appearance had largely resolved by 2 weeks. There was significant non-uniformity in the morphological characteristics of HLIM lesions between autofluorescence and reflectance images. The pattern of lesion evolution was also significantly different between imaging modalities. Analysis of the laser device showed its wavelength to be correctly listed, but the power was found to be 102.5-105 mW, as opposed to the <5 mW described on the label. CONCLUSION: While the immediate SD-OCT characteristics are highly specific for handheld laser -induced maculopathy, this finding can undergo rapid resolution in the span of several days. In the absence of this finding, other multimodal imaging clues and a careful history may aid in recognizing this diagnosis. A greater awareness regarding inaccurate labeling on some of these devices could help reduce the frequency of this preventable entity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".