Health continuum of care informatics knowledgebase framework
Bibliographic record
Abstract
All systems including health systems may be described in terms of processes that convert inputs to valued outputs. 'Management processes' set the strategy for 'demand/supply processes' which address health priority needs for quality of care services delivered via 'implementation processes'. These systems are very complex. Management processes involve many different perspectives, dimensions, objectives and systems, each with many states. Demand/supply processes involve numerous types of event chains, value streams and pathways of variable maturity, also with many states. Implementation processes involve several types of pathway flows, and interdependencies leading to decision tradeoffs. Taken together, these process variables and their states pose several billion process interaction options. This complexity complicates decision-making for optimizing health care benefits. A transparent common framework architecture has been developed within which all of these processes and their attributes and states many be inter-related and transparently navigated. It provides the ability to develop a common process knowledge base for understanding individual process events, pathway workflows, information flows, and value flows. It also facilitates assessment of key process interdependency tradeoffs that are required for business intelligence and informed management decision-making. A description of the framework, process operands and states is provided. An example illustrates an example of types of physiological/social tradeoffs for guiding breast cancer treatment options. A second example provides a navigation thread for prevention treatments such as vitamin D and related implications for adjustment of prevention, screening and diagnostic protocols.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 0.011 |
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".