The role of information technology in the explication and crystallization of tacit healthcare knowledge
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As a major contributor and user of healthcare knowledge, the healthcare enterprise requires intelligent knowledge management strategies to effectively exploit expert-quality healthcare knowledge. In this paper, we discuss a novel knowledge acquisition and representation strategy using healthcare scenarios. This strategy is complemented with an ontology- or thesaurus-based input standardization mechanism to limit the diverse nature of the healthcare experts’ input. We also present in detail an algorithm for healthcare knowledge crystallization that, in addition to simulating the process of annealing, employs the notion of nucleation and growth on healthcare knowledge. The crystallized knowledge potentially undergoes a repair process that employs analogical reasoning to identify less useful knowledge and transform them, with the aid of more established knowledge, into useful ones. We conclude by asserting that the strategies presented do provide an all-rounded approach in our efforts to bring quality healthcare knowledge and services to the masses.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it