Cost‐Effectiveness of the Geriatrician‐Led Comprehensive Geriatric Assessment in Different Healthcare Settings: An Economic Evaluation
Bibliographic record
Abstract
BACKGROUND: With a shortage of geriatricians, the appropriate distribution of geriatricians across healthcare settings (e.g., acute care, rehabilitation, or community clinics) is unknown. Our objective was to determine which setting(s) geriatricians should preferentially staff to be most economically attractive for the Canadian healthcare system. METHODS: We conducted a cost-effectiveness analysis using a two-dimensional microsimulation model. The model simulated a population of frail adults aged ≥ 65 years. The simulation was done over a lifetime horizon from the Ontario public payer perspective. Strategies included (1) usual care (baseline proportions of geriatrician CGAs in each setting), (2) acute care only (100% receive CGA in acute care), (3) community care only, (4) rehabilitation only, (5) acute care and community combined, (6) acute care and rehabilitation combined, (7) community and rehabilitation combined, and (8) acute care, community, and rehabilitation combined. Primary model outputs included quality-adjusted life months (QALMs), lifetime costs, and incremental cost-effectiveness ratios (ICERs). RESULTS: The acute care and rehabilitation combined strategy was undominated at a lifetime cost of C$139,987 and with an effectiveness of 42.09 QALM. At an ICER of C$1203 per QALM, the combination strategy of acute care, rehabilitation, and community clinics was cost-effective relative to acute care and rehabilitation, assuming a cost-effectiveness threshold of C$4167 per QALM (equivalent to C$50,000 per quality-adjusted life year). The other six strategies were dominated. When individually compared to usual care, all of the strategies were dominant or cost-effective. CONCLUSIONS: An undominated strategy of staffing geriatricians was in the acute care and rehabilitation settings, with the option of adding community clinics if cost and resources permit.
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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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".