Care trajectories around a first dementia diagnosis in patients with serious mental illness
Bibliographic record
Abstract
AIM: To develop a typology of care trajectories (CTs) 1 year before and after a first dementia diagnosis in individuals aged ≥65 years, with prevalent schizophrenia or bipolar disorder. METHODS: This was a longitudinal, retrospective cohort study using health administrative data (1996-2016) from Quebec (Canada). We selected patients aged ≥65 years with an incident diagnosis of dementia between 1 January 2014 and 31 December 2016, and a diagnosis of schizophrenia and/or or bipolar disorder. A CT typology was generated by a multidimensional state sequence analysis based on the "6 W" model of CTs. Three dimensions were considered: the care setting ("where"), the reason for consultation ("why") and the specialty of care providers ("which"). RESULTS: In total, 3868 patients were categorized into seven distinct types of CTs, with varying patterns of healthcare use and comorbidities. Healthcare use differed in terms of intensity, but also in its distribution around the diagnosis. For instance, whereas one group showed low healthcare use, healthcare use abruptly increased or decreased after the diagnosis in other groups, or was equally distributed. Other significant differences between CTs included mortality rates and use of long-term care after the diagnosis. Most patients (67%) received their first dementia diagnosis during hospitalization. CONCLUSIONS: Our innovative approach provides a unique insight into the complex healthcare patterns of people living with serious mental illness and dementia, and provides an avenue to support data-driven decision-making by highlighting fragility areas in allocating care resources. Geriatr Gerontol Int 2024; 24: 577-586.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".