Assessing postoperative cognitive dysfunction using 3D multiple object tracking in open heart surgery patients
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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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".