Development and validation of a low-cost microsurgery Ear Trainer for low-resource settings
Bibliographic record
Abstract
OBJECTIVE: Chronic suppurative otitis media is a neglected condition affecting up to 330 million people worldwide, with the burden of the disease in impoverished countries. The need for non-governmental organisations to hardwire training into their programmes has been highlighted. An ear surgery simulator appropriate for training in resource-poor settings was developed, and its effectiveness in facilitating the acquisition of headlight and microsurgical skills necessary to safely perform procedures via the ear canal was investigated. METHODS: Face validity was assessed via questionnaires. Six tasks were developed: a headlight foreign body removal task, and microscope tasks of foreign body removal, ventilation tube insertion, tympanomeatal flap raising, myringoplasty and middle-ear manipulation. Participants with varying ENT experience were video-recorded performing each task and scored by a blinded expert observer to assess construct validity. RESULTS: Face validity results confirmed that our Ear Trainer was a realistic representation of the ear. Construct validity results showed a statistically significant trend, with experts performing the best and those with limited experience performing better than novices. CONCLUSION: This study validates our Ear Trainer as a useful training tool for assessing headlight and microsurgical skills required to perform otological procedures.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".