Bibliographic record
Abstract
Learning Health Systems (LHS) is an international, open access, peer-reviewed journal published in collaboration with the University of Michigan.LHS aims to advance the interdisciplinary area of learning health systems by promoting research, scholarship, and dialogue focused on theory, complex issues, conceptual syntheses, educational models, solution designs, and system evaluations designed to achieve continuous rapid improvement in health and healthcare and to transform organizational practice.LHS research represents a new, trans-disciplinary science, and its contributors are researchers in fields such as behavioral, social, and organizational science; cognitive, information, and computer science; industrial and systems engineering, as well as other areas of expertise.Learning health systems research is focused across different levels of scale that include organizations, regional networks, and national and multi-national systems.The journal will publish empirical and theoretical studies in areas including but not limited to learning system theory, research methodology, measurement studies, digital knowledge objects and health knowledge management, human knowledge inter-action and making knowledge actionable, public health system learning, health knowledge markets and health system incentives to learn, health and healthcare problem-solving, health profession education, innovative clinical research paradigms, public and patient engagement in learning processes, data mining and knowledge generation, and infrastructure development and application.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.902 | 0.783 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".