A patient journey analysis: Identification and treatment of dementia with Lewy bodies
Bibliographic record
Abstract
Abstract Background Between 3.2%‐7.1% of dementia patients are diagnosed with DLB. There is limited research on the identification and treatment of DLB, indicating an unmet need for this condition.[2] [1] Hogan D, Fiest K, Roberts J, Maxwell C, Dykeman J, Pringsheim T, Jetté N. (2016). The Prevalence and Incidence of Dementia with Lewy Bodies: A Systematic Review. Canadian Journal of Neurological Sciences / Journal Canadien Des Sciences Neurologiques, 43(S1), S83‐S95. doi:10.1017/cjn.2016.2. [2] Monfared A, Meier G, Perry R, Joe D. Burden of disease and current management of Dementia with Lewy Bodies: a literature review. Neurol Ther, 2019, 8:289‐305. Method This claims‐data analysis examined commercial and Medicare Advantage with Part D (MAPD) enrollees aged ≥40 years with ≥2 DLB diagnoses from 01Jun2016–31May2018. The earliest DLB diagnosis was the index date. Continuous insurance enrollment was required from 24 months before to 12 months after index. Patient characteristics, diagnoses, medication utilization, symptoms (e.g. behavioral, movement changes), and imaging tests were measured in 6‐month periods pre‐ and post‐index. Result The 974 patients identified had mean age of 78.3 years, were 56.6% male, 90.0% were MAPD enrollees, and had a mean Charlson comorbidity score of 2.87 (SD=2.05). Generally, percentages of patients with ≥1 diagnosis in a dementia or symptom change category or with ≥1 pharmacy fill for an antipsychotic or behavioral health (BH) medication (e.g., antidepressants, anxiolytics) trended higher in each 6‐month period prior to index date, were highest in the 6 months beginning on index, and decreased slightly 7‐12 months post‐index. Across periods, 31%‐90% of patients had multiple types of dementia diagnoses. BH medications (52.4%‐63.9% with ≥1 BH medication fill across periods) were prescribed to DLB patients more often than were antipsychotics (14.2%‐31.5% with ≥1 antipsychotic fill). CT scans (10.6%‐27.5%) and MRIs (5.3%‐13.0%) were the most prevalent imaging tests. Conclusion Percentages of patients with imaging tests, DLB symptoms, diagnoses and treatments for DLB increased as patients’ first DLB diagnosis neared and occurred at lower percentages 7‐12 months after initial diagnosis.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".