A Unique Model for ONSD Part II: Inter/Intra-operator Variability
Bibliographic record
Abstract
OBJECTIVE: To evauluate our novel ultrasound model for measurement of optic nerve sheath diameter (ONSD) and determine the intra- and inter-operator variability associated with this technique. METHODS: We conducted ten measurements of ONSD per model amongst eight different models with a single experienced operator to examine intra-operator variability. Similarly, we had seven different operators measure the OSND twice in eight different models, in order to determine inter-operator variability analyzed with a three level linear statistical model. RESULTS: For intra-operator variability, the intra-cluster correlation coefficients for the experienced and novice operators were 0.643 and 0.453 respectively. This displayed improvement in intra-operator variability with experience. The inter-cluster correlation coefficient was 0 for the group of novice operators, indicating negligible difference amongst multiple operators in measuring any given model of ONSD. A strong, statistically significant, linear relationship between the actual model disc size and the ultrasound ONSD measures was identified, implying the reliability of the images produced by our novel model. CONCLUSIONS: Utilizing a novel model for ONSD ultrasonography, we have determined the intraoperator reliability of ONSD measurement to be moderate, with no appreciable difference amongst multiple operators. Improvement in measurement reliability has been demonstrated between expert and novice operators with our model, indicating the potential benefit of simulation platforms for teaching the technique of ONSD ultrasound.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".