The Association of Preoperative Trail Making Tests With Postoperative Delirium
Bibliographic record
Abstract
Aims The aim of the present study was to investigate the preoperative Trail Making Test (TMT) and its association with postoperative delirium. Materials and methods This cross-sectional, observational study consisted of 51 patients admitted to the surgical ward for any planned operative procedure. Consenting patients provided their sociodemographic information, and the Hospital Anxiety and Depression Scale (HADS), Montreal Cognitive Assessment (MoCA) test, and Trail Making Test (TMT) were applied. Results A total of 51 patients (66.7% male and 33.3% female) were categorized as the "normal" group (n=34), completing TMT in time, and the "slow" group (n=17). The mean age was 45.05 ± 13.69 for the normal group and 44.29 ± 10.95 for the slow group. The HADS score mean was 15.02 ± 9.52 and 11.64 ± 5.73, respectively, for these two groups (t = -1.577; degrees of freedom {df} = 47.11; p = 0.121). However, the "normal" group scored significantly higher MoCA scores in comparison to the slow group (26.35 ± 1.06 and 24.29 ± 1.10, respectively) (t = -6.410; df = 49; p = 0.000). Conclusions The study shows that the TMT can indicate effectively the cognitive decline in preoperative patients, which predicts postoperative delirium.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".