Integration of Human Factors in Surgery: Interdisciplinary Collaboration in Design, Development, and Evaluation of Surgical Technologies
Bibliographic record
Abstract
Research in surgical intervention and technology development is increasingly interdisciplinary. Despite the great potential of working in this way, recent research suggests that interdisciplinary collaborations and competing stakeholder interests can be challenging to initiate and manage, with the result that knowledge and expertise from different fields are not always well integrated. The aim of this workshop is to bring together stakeholders from HCI, surgical science, and surgical practice and technology to investigate the potential of interdisciplinary collaboration, specifically identifying actionable strategies to coordinate and improve efforts towards designing, developing, evaluating, and iterating on the next generation of surgical solutions. The workshop will address current limitations in interdisciplinary collaboration, and identify opportunities for surgical technology stakeholders to make contributions across the entire development life cycle. In the longer term, the workshop will contribute towards the development of a pragmatic collaboration framework encompassing diverse research paradigms, compatible with surgical practice, and supportive of longitudinal evaluation.
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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 | 0.107 | 0.115 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".