Usefulness of the Short IQCODE for Predicting Postoperative Delirium in Elderly Patients Undergoing Hip and Knee Replacement Surgery
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: The prevalence of postoperative delirium in elderly patients is >30%. The objective of this prospective study was to determine the usefulness of the short form of the Informant Questionnaire on COgnitive Decline in the Elderly (short IQCODE) to predict the occurrence of postoperative delirium after elective hip and knee arthroplasty in the elderly. METHODS: Consecutive patients, 60 years and older, who were admitted for elective hip or knee arthroplasty were included. The preoperative cognitive status was determined using the Mini-Mental State Examination (MMSE) and the short IQCODE. Postoperative delirium was diagnosed using the Confusion Assessment Method. Logistic regression was used to analyze the links between the preoperative test scores and the outcome of postoperative delirium. RESULTS: One hundred and one patients completed the study (mean age 73.6 +/- 6.6 years). The mean +/- SD MMSE score was 26 +/- 3, and the mean short IQCODE score was 50.7 +/- 6.2. Postoperative delirium developed in 15 patients (14.8%). A short IQCODE score >50 was significantly associated with postoperative delirium (OR 12.7, 95% CI 1.4-115.5; p = 0.02). CONCLUSIONS: The short IQCODE appears to be a useful tool to predict the risk of postoperative delirium in elderly patients undergoing elective surgery. Detecting this complication could be of great interest to improve the postoperative survey of elderly patients.
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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 | 0.009 |
| 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) | 0.001 | 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".