An Empirical Study of Different Diagnostic Criteria for Delirium Among Elderly Medical Inpatients
Bibliographic record
Abstract
This study compared the sensitivity and specificity of DSM-IV criteria for delirium with the sensitivity and specificity of DSM-III and ICD-10 criteria among elderly medical inpatients with or without dementia. Secondary objectives were to examine the effect of changing the definition of criterion A on sensitivity and specificity and to compare the sensitivity and specificity of different numbers of symptoms of delirium. A total of 322 elderly patients who had been admitted from the emergency department to the medical services were classified into one of four groups using DSM-III-R criteria: delirium and dementia (n = 128), delirium only (n = 40), dementia only (n = 94), and neither (n = 60). The sensitivity and specificity of DSM-IV, DSM-III, and ICD-10 criteria were determined against DSM-III-R criteria using three definitions of criterion A (clouding of consciousness only, clouding of consciousness and inattention, clouding of consciousness or inattention). When criterion A was defined as clouding of consciousness or inattention, the sensitivity and specificity of DSM-IV, DSM-III, and ICD-10 criteria were 100% and 71%, 96% and 91%, and 61% and 91%, respectively. The results were similar among patients with or without dementia. The lower specificity of DSM-IV was accounted for by its inclusion of patients who did not show disorganized thinking. DSM-IV criteria for delirium are the most inclusive criteria to date for elderly medical patients with or without dementia.
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 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 | 0.036 | 0.310 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".