Ordering Eyeglasses Using 3D Head Scan Technology versus Established Online and Storefront Clinic Methods
Bibliographic record
Abstract
SIGNIFICANCE: This study investigates how a new smartphone scanning technology compares with established online and storefront vendors in providing remote measurement and adjustment of prescription eyeglasses. PURPOSE: This study aimed to evaluate a new technology for ordering prescription eyeglasses online. METHODS: Thirty participants with 2.00 to 2.75 D of presbyopia (aged 49 to 74 years) were asked to order eyeglasses with progressive addition lenses from four vendors: one online vendor using a new head scan technology (Topology, San Francisco, CA), two established Web site-based vendors (vendors A and B), and one storefront vendor: Université de Montréal's Vision Clinic (UMVC). The resulting measurements were compared with those of opticians. Participant-reported preferences on visual and physical comfort of eyeglasses were collected after 15-minute trials of eyeglasses from each vendor. RESULTS: Pupillary half-distance measured with Topology matched optician measurements, but online vendors A and B diverged (mean difference, - 0.80 mm [ Z = -2.79; P = .005]). Topology and vendor B segment addition heights were similar to optician measurements, but vendor A diverged (mean, -1.40 mm [ Z = -2.58; P = .01]). The personalization parameter values obtained with Topology were different from optician measurements for pantoscopic angle (-5.30° [ Z = -4.12; P < .001]) and face wrap angle (+1.25° [ Z = -2.94; P = .003]). The UMVC eyeglasses scored best for adjustment (8.71/10 [ Z = -5.53; P < .001]), with Topology coming second (7.23/10). Topology scores were equivalent to UMVC scores for all eight items of patient-reported preferences (nonparametric Friedman analysis of variance, P < .05). CONCLUSIONS: Basic lens centration measurements obtained with Topology compare well with those of opticians, but some aspects of the methodology for measuring personalization parameters could be improved. In comparison with two established online vendors, resulting measurements with Topology are more consistent. Initial wearer satisfaction with Topology eyeglasses was also better.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".