P.054 The use of robotic technology to define post-operative neurological dysfunction in patients undergoing coronary bypass surgery: a feasibility study
Bibliographic record
Abstract
Background: Cognitive dysfunction following coronary artery bypass surgery is a regular occurrence, but its cause is still unknown. In order to devise strategies to mitigate this acquired disability, a precise and quantitative description of the post-operative neurocognitive phenotype is necessary. This study is designed to assess the feasibility of using the KINARM robot to quantify the changes in the neurological function after cardaic surgery. Methods: Patients without prior history of cognitive dysfunction were recruited from the pre-operative cardiac surgery clinic, and underwent pre-operative assessment with the KINARM. The KINARM provides a quantitative assessment of the neurocognitive control of the upper limbs. During bypass surgery, brain tissue oxygen levels were measured with near-infrared spectroscopy. Patients were reassessed with the KINARM post-operatively at 3 months. Results: To date, 12 participants have been recruited (mean age = 65 years, all male). On straightforward tasks, such as visually guided reaching, the majority of patients scored within the normal range, both pre- and post-operatively. In more complex tasks, required visuospatial and executive functioning, post-operative deficits were more pronounced. Conclusions: It is feasible to use the KINARM robot to provide a quantitative measurement of the neurocognitive phenotype of patients after cardiac surgery.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".