‘You see?’ Teaching and learning how to interpret visual cues during surgery
Bibliographic record
Abstract
CONTEXT: The ability to interpret visual cues is important in many medical specialties, including surgery, in which poor outcomes are largely attributable to errors of perception rather than poor motor skills. However, we know little about how trainee surgeons learn to make judgements in the visual domain. OBJECTIVES: We explored how trainees learn visual cue interpretation in the operating room. METHODS: A multiple case study design was used. Participants were postgraduate surgical trainees and their trainers. Data included observer field notes, and integrated video- and audio-recordings from 12 cases representing more than 11 hours of observation. A constant comparative methodology was used to identify dominant themes. RESULTS: Visual cue interpretation was a recurrent feature of trainer-trainee interactions and was achieved largely through the pedagogic mechanism of co-construction. Co-construction was a dialogic sequence between trainer and trainee in which they explored what they were looking at together to identify and name structures or pathology. Co-construction took two forms: 'guided co-construction', in which the trainer steered the trainee to see what the trainer was seeing, and 'authentic co-construction', in which neither trainer nor trainee appeared certain of what they were seeing and pieced together the information collaboratively. Whether the co-construction activity was guided or authentic appeared to be influenced by case difficulty and trainee seniority. Co-construction was shown to occur verbally, through discussion, and also through non-verbal exchanges in which gestures made with laparoscopic instruments contributed to the co-construction discourse. CONCLUSIONS: In the training setting, learning visual cue interpretation occurs in part through co-construction. Co-construction is a pedagogic phenomenon that is well recognised in the context of learning to interpret verbal information. In articulating the features of co-construction in the visual domain, this work enables the development of explicit pedagogic strategies for maximising trainees' learning of visual cue interpretation. This is relevant to multiple medical specialties in which judgements must be based on visual information.
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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 | 0.018 |
| 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".