Testing Pediatric Acuity With an iPad: Validation of “Peekaboo Vision” in Malawi and the UK
Bibliographic record
Abstract
Purpose: To evaluate two builds of the digital grating acuity test, “Peekaboo Vision” (PV), in young (6–60 months) populations in two hospital settings (Malawi and United Kingdom). Methods: Study 1 evaluated PV in Blantyre, Malawi (N = 58, mean age 33 months); study 2 evaluated an updated build in Glasgow, United Kingdom (N = 60, mean age 44 months). Acuities were tested-retested with PV and Keeler Acuity Cards for Infants (KACI). Bland-Altman techniques were used to compare results and repeatability. Child engagement was compared between groups. Study 2 included test-time comparison. Results: Study 1 (Malawi): The mean difference between PV and KACI was 0.02 logMAR with 95% limits of agreement (LoA) of 0.33 to 0.37 LogMAR. On test-retest, PV demonstrated 95% LoA of −0.283 to 0.198 logMAR with coefficient of repeatability (CR) 0.27. KACI demonstrated 95% LoA of −0.427 to 0.323 logMAR, and larger CR was 0.37. PV evidenced higher engagement scores than KACI (P = 0.0005). Study 2 (UK): The mean difference between PV and KACI was 0.01 logMAR; 95% LoA was −0.413 to 0.437 logMAR. Again, on test-retest, PV had narrower LoA (−0.344 to 0.320 logMAR) and lower CR (0.32) versus KACI, with LoA −0.432 to 0.407 logMAR, CR 0.42. The two tests did not differ in engagement score (P = 0.5). Test time was ∼1 minute shorter for PV (185 vs. 251 s, P = 0.0021). Conclusions: PV gives comparable results to KACI in two pediatric populations in two settings, with benefits in repeatability indices and test duration. Translational Relevance: Leveraging tablet technology extends reliable infant acuity testing to bedside, home, and rural settings, including areas where traditional equipment cannot be financed.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".