Accuracy and Repeatability of Self‐Measurement of Interpupillary Distance
Bibliographic record
Abstract
PURPOSE: To determine the accuracy and repeatability of participants determining their own interpupillary distance (PD). METHODS: Fifty-two healthy and naïve participants were enrolled and analyzed. All participants analyzed were without strabismus. Participants had PD measurements taken by a trained examiner using both a PD rule and an optical pupillometer. Participants then, following online instructions measured their own PD in a mirror, measured a friend's PD and used an online application downloaded to an IPod. Measurements were repeated twice for each type, and the pupillometer results were considered the gold standard (referent). RESULTS: The mean difference between the examiner PD rule measurement and the pupillometer were +0.59 mm [95% limits of agreement (LoA) -0.69 to +1.88], pupillometer-self +0.46 mm (-5.22 to +6.14), pupillometer-friend +2.00 mm (-3.80 to +7.81), and pupillometer-App -3.24 mm (-3.09 to +9.57). Measurements of repeatability using the 95% LoA for the examiner are -0.79 to 0.73 mm for the pupillometer and -1.04 to +1.20 mm for the PD rule. Participants' repeatability for the self-measurement (mirror) was -3.61 to +4.75 mm, employing a friend was -3.74 to +3.94 mm, and using the IPod application was -6.63 to +6.51 mm. CONCLUSIONS: Participants' ability to measure their own PD using techniques and applications available via the Internet result in poor accuracy and poor repeatability.
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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 | 0.006 | 0.024 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".