Reliability of Montreal Cognitive Assessment for assessment of postoperative cognitive dysfunction in elderly patients undergoing spinal surgery: a comparison with Mini-Mental State Examination
Bibliographic record
Abstract
Objective To evaluate the reliability of Montreal Cognitive Assessment (MoCA) for assessment of postoperative cognitive dysfunction (POCD) in elderly patients undergoing spinal surgery by comparing it with Mini-Mental State Examination (MMSE).Methods Sixty-three elderly patients with POCD (POCD group) and another 63 elderly patients with mental health after operation during the corresponding period (control group),of both sexes,aged 65-75 yr,undergoing spinal surgery under general anesthesia,were studied.POCD was assessed using MoCA and MMSE.The time for completion of assessment for the two methods was recorded,and the sensitivity and specificity were calculated.Results There was no significant difference in the time for completion of assessment between MoCA and MMSE (P ＞ 0.05).The sensitivity and specificity of MoCA in assessing POCD were 90％ and 84％,respectively,and of MMSE were 30％ and 97％,respectively.Compared with MMSE,the sensitivity of MoCA in assessing POCD was significantly increased,while the specificity of MoCA in assessing POCD was decreased (P ＜ 0.05 or 0.01).Conclusion MoCA can be applied for assessment of POCD in elderly patients undergoing spinal surgery. Key words: Aged; Cognitive disorders; Montreal cognitive assessment; Mini-mental state examination
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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 | 0.002 | 0.007 |
| 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 | 0.000 |
| 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 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".