Development and implementation of microsimulation models of neurological conditions.
Bibliographic record
Abstract
BACKGROUND: As part of a program of the first National Population Health Study of Neurological Conditions launched in 2009, a series of microsimulation models of neurological conditions (called POHEM-Neurological meta-model) was developed to project health and economic impacts of seven neurological conditions (NCs)-Alzheimer's disease and other dementias, cerebral palsy, epilepsy, multiple sclerosis, Parkinson's disease, traumatic brain injury, and traumatic spinal cord injury-over a 20-year horizon. DATA AND METHODS: The common framework of the seven models allows for dynamic, continuous-time, discrete-event simulation of synthetic large populations in which persons are subject to the risk of developing the NC under study and are assigned a value of functional health and a probability of receiving a caregiver and of entering long-term care. Calculations for transitions are done every year over the life course, and costs are accumulated throughout the life of the synthetic person. The need to reconcile empirical estimates of incidence and mortality with prevalence required implementation of "cure" parameters for two of the NCs. RESULTS: The POHEM-Neurological meta-model integrates the latest Canadian microdata on neurological conditions and satisfies most criteria for validation of microsimulation models, including conceptualization, computer implementation, assessment of output plausibility, and comparison with external data. Limitations include an absence of risk factors and the lack of uncertainty measures. INTERPRETATION: The POHEM-Neurological meta-model has been useful for projections of health and economic impacts of NCs on persons affected and their caregivers, and allows for comparison of specific scenarios to the base case.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".