Development of a novel simulation‐based task trainer for management of retrobulbar hematoma
Bibliographic record
Abstract
BACKGROUND: Retrobulbar hematoma (RH) is a rare but devastating complication of sinus surgery. It is treated initially with a lateral canthotomy and cantholysis at the bedside. Due to the high stakes and urgency of this complication, teaching this in the clinical setting is difficult. The objective of this study was to develop a cadaveric model for addressing this problem. METHODS: A fresh-frozen human cadaveric model of RH was created using a Foley catheter to simulate elevated intraocular pressure. Residents who participated in an emergencies in otolaryngology-head & neck surgery "boot camp" were included in the study. A survey measuring confidence levels in performing lateral canthotomy and cantholysis was administered. After completing the skill station, a postintervention survey was administered to assess the confidence of the learner as well as fidelity and usefulness of the task trainer. RESULTS: Thirty-three residents participated in the boot camp. Residents rated their confidence preintervention at 1.3/5, which suggests the majority were unable to perform the procedure. After using the model, residents rated their confidence at 3.5/5, which falls between basic knowledge and reasonably confident; this improvement achieved statistical significance (p < 0.0001). The fidelity of the model was rated 3.9/5; a score of 4 is defined as realistic. The residents rated the usefulness of the model as 4.7; a score of 5 is defined as very useful. CONCLUSION: A cadaveric model of RH was successfully developed. This novel simulator was perceived to be useful, realistic, and effective by junior residents.
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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 | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".