Evaluation of a new technique to evaluate the visual pursuit in infants
Bibliographic record
Abstract
Abstract Purpose This study presents a new technique for recording visual pursuit and its evaluation. In infants, visual acuity is usually estimated using behavioral methods. Nowadays, an objective response can be recorded using eye trackers based on the corneal reflex and pupil positions. However, these systems have limitations, such as large eye eccentricity and parasite reflections on tears or eyeglasses. To avoid these constraints, a new technique has been developed and evaluated. Methods The system is composed of a stimulation monitor equipped with a near infra‐red light source and a video camera. A real‐time analysis of the video identifies the head position, using a reflective dot placed between the patient’s eyes. The gaze orientation is obtained from the relative position of the reflective dot and the eye pupils. Clinical tests were carried out on 90 infants (3 months to 4 years). Gabor type stimuli moving along the horizontal axis were presented while head and eye movements were recorded. Results Visual pursuit was recorded in 84 infants, some of them with nystagmus or large eye deviation. The visual tracking patterns matched with the literature, i.e. saccadic for the youngest ages and smooth in older normal children. Conclusion Results show that the new technique is robust and efficient for recording infants’ visual pursuit under clinical conditions. Further tests are planned to evaluate if visual acuity estimation agrees with clinical exams. Commercial interest
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".