The Clinical Relevance of Information Index (<scp>CRII</scp>): assessing the relevance of health information to the clinical practice
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
BACKGROUND: The high volume of health information creates a need for processes and tools to select, evaluate and disseminate relevant information to health professionals in clinical practice. OBJECTIVES: To introduce an index of the clinical relevance of information and to show that it is different from existing measures. METHODS: A conceptual model of knowledge translation was developed to explain the need for a new index, whose application was verified by an exploratory study with two (quantitative and qualitative) phases. The Clinical Relevance of Information Index (CRII) was defined employing descriptive statistical analyses of assessments performed by health professionals. The model and the CRII were applied in a primary healthcare context. RESULTS: The CRII was applied to 4574 relevance assessments of 194 evidence synopses. The assessments were performed by 41 family physicians in 2008. The CRII value of each synopsis was compared with the number of citations received by its corresponding research paper and with the level of evidence of the study, presenting weak correlation with both. CONCLUSION: The CRII captures aspects of information not considered by other indices. It can be a parameter for information providers, institutions, editors, as well as health and information professionals targeting knowledge translation.
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 | 0.046 | 0.072 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.034 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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