Understanding the role of induction, intensions and extensions in pragmatic clinical research and practice
Bibliographic record
Abstract
BACKGROUND: Pragmatic clinical research methods are poorly understood, but essential to practice outcome-based medical or surgical care. Pragmatic research aims to verify the connections between medical knowledge and the reality of practice. Its methods can be understood by reviewing the problems of induction, as well as the related linguistic and mathematical notions of intensions and extensions. METHODS: We briefly review the source of problems with using inductive methods to gain knowledge, and the relationships between language, mathematics and reality. We discuss linguistic 'sense' and 'reference', and the set-theory terms 'intensions' and 'extensions', which define the relationship between individuals and whichever pertinent collection these individuals comprise. Both concepts are essential to understand pragmatic medical research and evidence-based practice. RESULTS: Pragmatic clinical research can be explained in terms of testing (in reality) the repeatability of various inductive referential and inferential steps used in clinical practice - from reliability, diagnostic accuracy, and prognostic studies to pragmatic trials. All pragmatic studies aim to verify the relationship between the extensions of the notions of symptoms, diagnoses, prognoses, treatments, and outcomes. The concepts of intensions and extensions also serve to understand 'statistical significance' in analyzing trial results, as well as problems related to eligibility criteria and subgroup analyses. The results of clinical studies can be generalized to the extent that they have been tested in numerous and widely different individuals. CONCLUSION: The notions of sense and reference, and of intensions and extensions, help explain the role pragmatic clinical research methods can play in optimizing care.
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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 | 0.309 | 0.308 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.005 | 0.154 |
| Scholarly communication | 0.021 | 0.052 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".