Failure to account for practice effects leads to clinical misinterpretation of cognitive outcome following carotid endarterectomy
Bibliographic record
Abstract
Carotid endarterectomy (CEA) is a surgical procedure to remove stenotic atherosclerotic plaque from the origin of the carotid artery to reduce the risk of major stroke. Its impact on postoperative cognitive function (POCF) remains controversial; complicated, in part, by a traditional failure to account for practice effects incurred during consecutive psychometric testing. To address this for the first time, we performed psychometric testing (learning and memory, working memory, attention and information processing, and visuomotor coordination) in 15 male patients aged 68 ± 8 years with symptomatic carotid stenosis the day before and 24 h following elective CEA (two consecutive tests, 48 h apart). Multiple baselining was also performed in a separate cohort of 13 educationally, anthropometrically and age-matched controls (63 ± 9 years) not undergoing revascularization at identical time points with additional measures performed over a further 96 h (four consecutive tests, each 48 h apart). A single consecutive test in the control group resulted in progressive improvements in learning and memory, working memory, and attention and information (P < 0.05 vs. Test 1), with three tests required before cognitive performance stabilized. Following correction for practice effects in the patient group, CEA was associated with a deterioration rather than an improvement in learning and memory as originally observed (P < 0.05). These findings highlight the potential for the clinical misinterpretation of POCF unless practice effects are taken into account and provide practical recommendations for implementation within the clinical setting.
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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 | 0.011 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 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".