INTRAOPERATIVE IMAGE NAVIGATION: EXPERIMENTAL STUDY OF THE FEASIBILITY AND SURGEON PREFERENCE BETWEEN A STERILE ENCASED NINTENDO WII<sup>TM</sup> REMOTE AND STANDARD WIRELESS COMPUTER MOUSE
Bibliographic record
Abstract
Diagnostic imaging plays an important role in the operating room, providing surgeons with a reference and surgical plan. Surgeon autonomy in the operating room has been suggested to decrease errors that stem from communication mistakes. A standard computer mouse was compared to a wireless remote-control style controller for computer game consoles (Wiimote) for the navigation of diagnostic imaging studies by sterile personnel in this prospective survey study. Participants were recruited from a cohort of residents and faculty that use the surgical suites at our institution. Outcome assessments were based on survey data completed by study participants following each use of either the mouse or Wiimote, and compared using an analysis of variance. The mouse was significantly preferred by the study participants in the categories of handling, accuracy and efficiency, and overall satisfaction (P <0.05). The mouse was preferred to both the Wiimote and to no device, when participants were asked to rank options for image navigation. This indicates the need for the implementation of intraoperative image navigation devices, to increase surgeon autonomy in the operating room.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.008 | 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".