Diagnosis and Disruption: Population‐Level Analysis Identifying Points of Care at Which Transitions Are Highest for People with Dementia and Factors That Contribute to Them
Bibliographic record
Abstract
OBJECTIVES: To examine transitions that individuals with dementia experience longitudinally and to identify points of care when transitions are highest and the factors that contribute to those transitions. DESIGN: Population-based 10-year retrospective cohort study from 2000 to 2011. SETTING: General community. PARTICIPANTS: All individuals aged 65 and older newly diagnosed with dementia in British Columbia, Canada. MEASUREMENTS: The frequency and timing of transitions over 10 years, participant characteristics associated with greater number of transitions, and the influence of recommended dementia care and high-quality primary care on number of transitions. RESULTS: Individuals experience a spike in transitions during the year of diagnosis, driven primarily by hospitalizations, despite accounting for end of life or newly moving to a long-term care facility (LTCF). This occurs regardless of survival time or care location. Regardless of survival time, individuals not in LTCFs experience a marked increase in hospitalizations in the year before and the year of death, often exceeding hospitalizations in the year of diagnosis. Receipt of recommended dementia care and receipt of high-quality primary care were independently associated with fewer transitions across care settings. CONCLUSION: The spike in transitions in the year of diagnosis highlights a distressing period for individuals with dementia during which unwanted or unnecessary transitions might occur and suggests a useful target for interventions. There is an association between recommended dementia care and outcomes and evidence of the continued value of high-quality primary care in a complex population at a critical point when gaps in continuity are especially likely.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".