A Proposed Decision-Making Framework for the Translation of In-Person Clinical Care to Digital Care: Tutorial
Bibliographic record
Abstract
UNSTRUCTURED: The continued demand for digital health requires that providers adapt thought processes to enable sound clinical decision making in digital settings. Providers report that lack of training is a barrier to providing digital healthcare. Physical exam techniques and hands-on interventions must be adjusted in safe, reliable and feasible ways to digital care and decision making may be impacted by modifications made to these techniques. We have proposed a framework for determining if a procedure can be modified to obtain a comparable result in a digital environment or if a referral to in-person care is required. The decision making framework developed using program outcomes of a digital physical therapy platform, and aims to alleviate provider barriers to providing digital care. This paper describes the unique considerations a provider must make when collecting background information, selecting procedures, executing procedures, assessing results, and determining if they can proceed with clinical care in digital settings.
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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 | 0.052 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".