Development of a novel multi‐stimulus ocular surface aesthesiometer system
Bibliographic record
Abstract
Aims/Purpose: To report the development of a novel, air‐based, aesthesiometer capable of producing and applying multiple stimuli of differing intensity, separated by time and/or ocular surface location. Methods: Individual micro‐blowers (Murata Manufacturing Co., Japan) were combined with an exit airflow nozzle into an integrated stimulus unit (ISU) that produced a controlled exit airflow through the nozzle exit. Stimulus intensity, duration, and repetition, as a feature of airflow, was managed for each ISU using a designed software. The software also controlled stimulus sequencing for multiple ISU in series. Stimulus airflow consistency and characteristics were assessed using two nozzle diameters (0.5 and 1.6 mm) for: (i) airflow pattern/trajectory; (ii) airflow surface dispersion; (iii) force of airflow; (iv) thermal effects; (v) time delay and spatial separation. Results: Stimulus characteristics: (i) airflow is coherent at proposed test distance (1 cm) and spread rate is constant irrespective of stimulus strength, airflow diameter at 1 cm: 1.8–2.1 mm; (ii) airflow disperses over surface and dispersion increases with increasing airflow rate; (iii) consistent, small force (10−4 N) is produced and varies with airflow rate; (iv) size of thermal effects depends on airflow rate; (v) single stimuli, repeat single stimuli with variable time‐delay, simultaneous multiple stimuli or with inter‐stimulus time delay can be produced. Conclusions: The novel ISU can produce repeatable, consistent air pulses (stimuli) under software control. Nozzle diameter has an influence on airflow coherence/dispersion. Single or multiple simultaneous stimuli with/without time delays, of differing intensity and duration, can be delivered to ocular surface. Multiple ISU allows presentation of multiple simultaneous stimuli that may provide an alternative method for assessing ocular surface sensitivity, and facilitate investigation of signal processing involved in ocular surface nerve summation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".