Validation of the Malay 3-Minute Diagnostic Interview for Confusion Assessment Method in a surgical population
Bibliographic record
Abstract
Background: Delirium is a common postoperative complication among elderly which can be easily missed and leads to poorer outcomes. The 3-Minute Diagnostic Assessment for Confusion Assessment Method (3D-CAM) is a short and structured tool to assess delirium by healthcare staff with minimal training. This study aimed to validate the translated Malay 3D-CAM (M3D-CAM) in postoperative surgical patients. Methods: In this prospective diagnostic study, 3D-CAM was translated into Malay and two assessors (1 and 2) independently interviewed surgical patients above 65 years old with M3D-CAM on postoperative day one. A psychiatrist diagnosed postoperative delirium according to the Diagnostic and Statistical Manual of Mental Disorders 5th Edition (DSM-5) as the reference standard. The sequence of examinations was done randomly with all results blinded to each other and the diagnostic characteristics of M3D-CAM analysed with k coefficient used to evaluate reliability. Results: A total of 427 patients were screened, 111 recruited with a final 100 paired interviews completed. Their mean age was 72 (± 6) years old. Two-thirds of patients were proficient in Malay and English, therefore assessed in both 3D-CAM and M3D-CAM. Delirium was identified in 11% and 12% of patients by assessors 1 and 2 respectively while compared to DSM-5, M3D-CAM had 80% and 90% sensitivity with 96.7% and 97.7% specificity. M3D-CAM had excellent inter-rater reliability (85%), substantial parallel reliability (70%) and features 1 and 3 with substantial parallel agreement (p <0.001). Conclusion: This study demonstrated that M3D-CAM is reliable and valid for delirium assessment in the postoperative setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".