Study of the Spectrum of Treatable Dementias: Insights from a Large South Asian Cohort (TREAT-Dem Study)
Bibliographic record
Abstract
ABSTRACT Background and Purpose: Dementia affects millions globally, with a subset of cases potentially reversible. This study evaluates the incidence, clinical markers and treatment outcomes of reversible dementias (ReDem). Method: This retrospective study included 370 ReDem cases from 1810 dementia patients. The ReDem cohort was split into potentially reversible dementias (PRD) and dual etiology (DE) groups. PRD encompassed secondary, potentially treatable dementia conditions, while DE included primary degenerative dementia (DD) with ≥1 uncontrolled comorbidity or new disease that worsened symptoms. Results: ReDem cases comprised 20.4% ( n = 370 out of 1810) of dementia patients, with ReDem patients being younger (mean 56.2 vs. 61.9 years, p < 0.001) and exhibiting shorter illness durations than DD patients ( p < 0.001). Key red flags, including young age (<45 years) at onset (DD = 8.6% vs. ReDem = 18.1%), fluctuation in symptoms (DD = 3.4% vs. ReDem = 11.6%), rapid cognitive decline (DD = 6.9% vs. ReDem = 18.4), high-risk exposures (DD = 0.1% vs. ReDem = 0.8%), high-risk behavior (DD = 0.1% vs. ReDem = 2.4%) and incongruent neuropsychological findings(DD = 1.0% vs. 12.7%), were significantly more frequent in ReDem cases ( p < 0.05). Odds increased with each red flag present (≥1: OR = 5.94; ≥2: OR = 20.69; ≥3: OR = 25.14, p < 0.05). Reversible etiologies included immune (20.0%), neuroinfectious (6.6%), psychiatric (7.6%), nutritional/metabolic (10.5%), neurosurgical (14.6%) and other causes (12.2%). Of the 41% (152/370) followed, 19 expired, 63.9% (85/133) reported subjective improvement, and 31.6% (42/133) showed clinical dementia rating improvement. Discussion and Conclusion: This large-scale study underscores the importance of comprehensive diagnostic evaluations for ReDem. Identifying and treating reversible conditions and comorbidities in DD can improve patient outcomes, emphasizing the need for thorough evaluations in memory clinics and targeted interventions in dementia care.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".