Development of the electronic consultation long-term care utilization and savings estimator tool to model the potential impact of electronic consultation for residents living in long-term care
Bibliographic record
Abstract
Ageing populations have resulted in more patients living in long-term care or nursing homes, where they face challenges to accessing prompt specialist care exacerbated in many cases by physical or cognitive decline. Electronic consultation has demonstrated an ability to improve access to specialist care for vulnerable groups and offers a potential solution to this gap in care. To support electronic consultation's uptake among long-term care homes, we created the electronic consultation long-term care utilization and savings estimator, an Excel-based tool that estimates the number of off-site appointments that patients in a long-term care home could avoid through electronic consultation, along with the consequent time and cost savings. In this brief report, we discuss the electronic consultation long-term care utilization and savings estimator's creation and function, and provide a case study using long-term care data to demonstrate its potential impact. We anticipate the electronic consultation long-term care utilization and savings estimator will be a highly impactful tool and intend to test it in real-world conditions following the relaxation of COVID-19 restrictions.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".