The association of attentional foci and image interpretation accuracy in novices interpreting lung ultrasound images: an eye-tracking study
Why this work is in the frame
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Bibliographic record
Abstract
It is unclear, where learners focus their attention when interpreting point-of-care ultrasound (POCUS) images. This study seeks to determine the relationship between attentional foci metrics with lung ultrasound (LUS) interpretation accuracy in novice medical learners. A convenience sample of 14 medical residents with minimal LUS training viewed 8 LUS cineloops, with their eye-tracking patterns recorded. Areas of interest (AOI) for each cineloop were mapped independently by two experts, and externally validated by a third expert. Primary outcome of interest was image interpretation accuracy, presented as a percentage. Eye tracking captured 10 of 14 participants (71%) who completed the study. Participants spent a mean total of 8 min 44 s ± standard deviation (SD) 3 min 8 s on the cineloops, with 1 min 14 s ± SD 34 s spent fixated in the AOI. Mean accuracy score was 54.0% ± SD 16.8%. In regression analyses, fixation duration within AOI was positively associated with accuracy [beta-coefficients 28.9 standardized error (SE) 6.42, P = 0.002). Total time spent viewing the videos was also significantly associated with accuracy (beta-coefficient 5.08, SE 0.59, P < 0.0001). For each additional minute spent fixating within the AOI, accuracy scores increased by 28.9%. For each additional minute spent viewing the video, accuracy scores increased only by 5.1%. Interpretation accuracy is strongly associated with time spent fixating within the AOI. Image interpretation training should consider targeting AOIs.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it