Required knowledge for guideline panel members to develop healthcare related testing recommendations: a developmental study
Bibliographic record
Abstract
OBJECTIVES: To define the minimum knowledge required for guideline panel members (healthcare professionals and consumers) involved in developing recommendations about healthcare related testing. STUDY DESIGN AND SETTING: A developmental study with a multistaged approach. We derived a first set of knowledge components from literature and subsequently performed semistructured interviews with 9 experts. We refined the set of knowledge components and checked it with the interviewees for final approval. RESULTS: Understanding the test-management pathway, for example, how test results should be used in context of decisions about interventions, is the key knowledge component. The final list includes 26 items on the following topics: health question, test-management pathway, target population, test, test result, interpretation of test results and subsequent management, and impact on people important outcomes. For each item, the required level of knowledge is defined. CONCLUSION: We developed a list of knowledge components required for guideline panels to formulate recommendations on healthcare related testing. The list could be used to design specific training programs for guideline panel members when developing recommendations about tests and testing strategies in healthcare.
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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 | 0.064 | 0.298 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".