GRADE concept paper 9: rationale and process for creating a GRADE Ontology
Bibliographic record
Abstract
CONTEXT: As the rate of research production accelerates, the ability to efficiently and unambiguously communicate judgments relating to the synthesis, evaluation, and use of scientific information becomes paramount. PERSPECTIVE: Scientific information can be viewed as a "layered infrastructure" of data, evidence, knowledge, and use. The GRADE approach serves as a de facto data standard for this infrastructure, supporting movement between layers by reducing ambiguity in claims to knowledge (in the form of judgements of certainty in the evidence when answering research questions) and level of commitment to possible solutions to problems (in the form of strength of recommendations for interventions). PURPOSE: This GRADE concept paper outlines the structure, purpose, and potential benefits of the GRADE Ontology for (a) the creators of, educators in, and users of systematic reviews, health guidelines, and health technology assessments, and (b) the development of tools that help with conducting, finding, and summarising the same. This paper also presents the processes for the development and maintenance of the GRADE Ontology, a formalised terminology standard within GRADE that will support the efficiency, rigour, consistency, and interoperability of GRADE's use. PLAIN LANGUAGE SUMMARY: The rate of research production is increasing exponentially. It is therefore becoming increasingly important to quickly, efficiently, and unambiguously communicate the judgments made and processes used when doing research and using evidence to inform policy decisions. GRADE is a widely used approach to assessing certainty of evidence when answering research questions and making recommendations for health interventions, designed to help with the efficient and transparent evaluation and use of evidence. However, the absence of a formalized terminology standard within GRADE limits the efficiency with which the results of its use can be communicated. In response, the GRADE Ontology is being created. This concept paper outlines what an ontology is, how it helps with communicating scientific information, the specific benefits of the GRADE Ontology, and the processes for developing and maintaining a useful, valid ontology that supports the use of the GRADE approach.
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How this classification was reachedexpand
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 | 0.005 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".