The sensitivity and specificity of statistical rules for diagnosing delayed neurocognitive recovery with Montreal cognitive assessment in elderly surgical patients
Bibliographic record
Abstract
Delayed neurocognitive recovery (DNR) is common in elderly patients after major noncardiac surgery. This study was designed to investigate the best statistical rule in diagnosing DNR with the Montreal cognitive assessment (MoCA) in elderly surgical patients.This was a cohort study. One hundred seventy-five elderly (60 years or over) patients who were scheduled to undergo major noncardiac surgery were enrolled. A battery of neuropsychological tests and the MoCA were employed to test cognitive function at the day before and on fifth day after surgery. Fifty-three age- and education-matched nonsurgical control subjects completed cognitive assessment with the same instruments at the same time interval. The definition of the international study of postoperative cognitive dysfunction (ISPOCD 1) was adopted as the standard reference for diagnosing DNR. With the MoCA, the following rules were used to diagnose DNR: the cut-off point of ≤26; the 1 standard deviation decline from baseline; the 2 scores decline from baseline; and the Z score of ≥1.96. The sensitivity and specificity as well as the area under receiver operating characteristic curve for the above rules in diagnosis of DNR were calculated.The incidence of DNR was 13.1% (23/175) according to the ISPOCD1 definition. When compared with the standard reference, the 2 scores rule showed the best combination of sensitivity (82.6%, 95% confidence interval [CI] 67.1%-98.1%) and specificity (82.2%, 95% CI 76.2%-88.3%); it also had the largest area under receiver operating characteristic curve (0.824, 95% CI 0.728-0.921, P < .001). The cut-off point rule showed high sensitivity (95.7%) and low specificity (37.5%), whereas the 1 standard deviation and the Z score rules showed low sensitivity (47.8% and 21.7%, respectively) and high specificity (93.4% and 97.3%, respectively).Compared with the ISPOCD1 definition, the 2 scores rule with MoCA had the best combination of sensitivity and specificity to diagnose DNR.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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".