INTRODUCTION TO THE SPECIAL ISSUE ON CASE‐BASED REASONING IN THE HEALTH SCIENCES
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
There has been an explosion of interest in health sciences applications of case‐based reasoning (CBR), not only in the traditional CBR in medicine domain, but also in bioinformatics, enabling home health‐care technologies, CBR integration, and synergies between CBR and knowledge discovery. This special issue features the best papers from the third workshop on CBR in the health sciences, held at ICCBR‐05 in Madrid. It is the third in a series of exciting workshops, the first two of which were held at ICCBR‐03, in Trondheim, Norway, and at ECCBR‐04, in Madrid, Spain. The nine high‐quality papers introduced here represent the research and experience of twenty‐two authors working in eight different countries on a wide range of problems and projects. These papers illustrate some of the major trends of current research in CBR in the health sciences, and represent overall an excellent sample of the most recent advances of CBR in the health sciences.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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