Utility of online cognitive test in cognitive neurology unit
Bibliographic record
Abstract
Abstract Background Delirium is one of the most common complications in hospitalized elders and corresponds with a higher risk of cognitive decline and death. Numerous test batteries exist to assist clinicians in detecting delirium, but it continues to be undiagnosed in up to 88% of patients, particularly those with AD where “confusion” is mistakenly attributed to impaired baseline (1). This pilot study investigated the utility of a digital cognitive assessment (D‐Cog), in the form of an iPad game, which would allow for rapid and routine baseline testing in patients with AD so that delirium‐induced changes can be easily detected. Method While collecting traditional vital signs, medical assistants in the Beth Israel Deaconess Center (BIDMC) Cognitive Neurology Unit (CNU) prompted 67 willing patients to complete the D‐Cog assessment, an adaptive visuospatial span task, on an iPad. We then retrospectively reviewed the medical records of each patient to collect diagnosis, demographics, and pen‐and‐paper neuropsychological scores (e.g., MoCA or MMSE). Of the 67 patients (mean age 67, 29 female), 22 were clinically diagnosed with mild cognitive impairment (MCI) or varying severities of AD. Result The assessment was successfully completed by 52 participants. 15 participants, including 5 participants with moderate AD, experienced great difficulty during the task and were subsequently excluded. The 17 included participants with AD (mean age 75, 6 female) averaged a D‐Cog score of 4.1 and a MMSE score of 23.1. In comparison, a control group with no cognitive concerns (n = 16, mean age 60, 9 female) garnered a 6.7 average D‐Cog score and 29.3 MMSE score. The difference in D‐Cog scores was significantly different at p = 0.001. Conclusion The D‐Cog assessment is feasible in clinical practice for patients with a wide range of cognitive impairments. These scores can act as an attentional baseline to compare against later testing in the event of suspected delirium. While the D‐Cog is derived from traditional pen‐and‐paper testing, its digital framework offers new opportunities for adaptive and remote testing. Reference Fong TG, Davis D, Grodon ME, Albuquerque A, Inouye SK. The interface between delirium and dementia in elderly adults. Lancet Neruol. 2015; 14(8): 823–32
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".