A New Validation of Montreal Cognitive Assessment on a Large Sample of Elderly Patients Undergoing Elective Orthopedic Surgery.
Bibliographic record
Abstract
Objective: The hip and knee arthroplasties are reliable and suitable surgical procedures aimed at restoring the patients' functioning. The most representative age range for these replacement surgeries is between 65 and 84 years for females. With aging, the likelihood of developing cognitive deficits increases, and there is evidence that elderly patients undergoing surgery orthopedic are at higher risk of developing cognitive problems in the postoperative phase. The Montreal Cognitive Assessment (MoCA) is often used for cognitive evaluation, but different cut-offs and validations are available in the literature. Given the importance of the problem, in this work we studied a hospitalized population candidate for orthopedic surgery to determine a new specific validation of the MoCA to assess the risk of MCI. Method: We applied MoCA and Mini-Mental State Examination (MMSE) to a sample of 492 (333 women) hospitalized patients for knee (74%) or hip surgery. A non-parametric receiver operating characteristic (ROC) curve analysis was conducted to investigate the predictive accuracy of the MoCA to assess cognitive impairment, using MMSE as the gold standard. Results: A score of 22.52 gives a sensitivity of 70% and a specificity of 78%. This value is providing a more coherent diagnosis with the MMSE as compared to the other cut-offs presented in the other available validations. No differences were found between patients in terms of age and gender, suggesting a general uniformity of the selected sample. Conclusions: Deepening the coherence in MCI diagnosis between MMSE and the other MoCA's scoring considered, our new cut-off seems reasonably better than previous Italian validation on an elderly population in matching MMSE classification.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".