An investigation on integrating eastern and western medicine with informatics
Bibliographic record
Abstract
Today, in many western countries, acceptance of alternate forms of healthcare such as Chinese medicine (CM) is increasing. In fact, countries such as Australia, Canada, and England are going so far as to set regulations, education, and standards regarding the practice of CM in these respective countries. Further, we can see the integration between western and Chinese medicine delivery of care and treatments in many instances. Information Systems and Information Technology (IS/IT) can be a key enabler in assisting this integration. The following study examines aspects of such integrations using IS/IT and identifies that CM IS/IT is more likely to succeed when there is synthesis between key aspects of the unique environment and user requirements. This perspective is supported theoretically by adapting Churchman’s Inquiring Systems to frame CM as a combination of Hegelian and Kantian inquiring systems with the support of Singerian, Lockean, and Leibnizian inquiring systems and Knowledge Management (KM) features. Based on this, the study then proposes a new design for a patient management system in clinics and hospitals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".