The Confusion Assessment Method Could Be More Accurate than the Memorial Delirium Assessment Scale for Diagnosing Delirium in Older Cancer Patients: An Exploratory Study
Bibliographic record
Abstract
BACKGROUND: Older people with cancer carry a high risk of delirium, an underdiagnosed syndrome due to its diagnostic complexity and often subtle presentation. Tools based on the Diagnostic and Statistical Manual of Mental Disorders (DSM) are available to different health professionals. Our aim is to assess the prevalence of delirium in older people with cancer in an inpatient unit and the accuracy of the Confusion Assessment Method (CAM) and Memorial Delirium Assessment Scale (MDAS). METHODS: This exploratory, cross-sectional study included people aged 65 years or older with a diagnosis of cancer and admitted to the medical oncology unit from June 2021 to December 2022. The diagnostic accuracy of CAM and MDAS was analyzed against the gold standard medical diagnosis based on DSM-5 criteria by two medical oncologists. The cutoff point for the MDAS was determined using a receiver-operating characteristics (ROC) curve. RESULTS: Among the 75 included patients (mean age 71.6 years, standard deviation 4.1; 52% males), the prevalence of delirium was 62.7%. The most prevalent types of cancer in patients with delirium were hematological and lung cancer. The scale with the highest diagnostic accuracy was the CAM, with a sensitivity of 100% and specificity of 86%, followed by the MDAS, with a sensitivity of 88% and specificity of 30%. The presence of cognitive impairment hindered the detection of delirium. CONCLUSIONS: The CAM scale was more accurate than the MDAS pre-existing cognitive impairment in our sample. Further studies are needed to analyze the diagnostic accuracy of delirium tools in older populations with cancer and in the presence of cognitive impairment.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".