Assessing postoperative cognitive dysfunction using 3D multiple object tracking in open heart surgery patients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Post-operative cognitive dysfunction is a common complication after heart surgery that affects up to 60% of all open-heart surgery patients. Despite its prevalence, limited attention has been given to different methods to retrain cognition in open-heart surgery patients. OBJECTIVE: To examine whether 3-dimensional multiple object tracking (3D MOT) can be used to detect changes in cognitive function in open-heart surgery patients. METHODS: In total, 16 open-heart surgery patients (age: 59.43 [Formula: see text] 12.99 years) from a Midwestern Canadian hospital were recruited. The patients completed a cognitive assessment, including 3D MOT and other standardized neurocognitive tests at 3 time points: 1 to 2 days pre-surgery, at discharge or 1-week post-surgery (whichever came first), and at 12-weeks post-surgery. RESULTS: No significant differences were detected between baseline and 1-week/discharge measurements on all measures. Patients improved significantly from 1-week/discharge to 12-weeks in 3D MOT scores. A similar yet non-significant ([Formula: see text] 0.07) trend was found on some neurocognitive tests (i.e., Montreal Cognitive Assessment). CONCLUSION: No significant decline from pre- to 1-week/discharge post-surgery was found on all measures. 3D MOT detected post-surgical cognitive changes in open-heart surgery patients. Future research is warranted to explore the potential of 3D MOT in retraining cognition after heart surgery.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it