041 Making Recommendations About Diagnostic Tests And Strategies: What Do Experts Say?
Bibliographic record
Abstract
Background Current practices in developing guidelines about the use of diagnostic tests and strategies (DTS) are out of step with the conceptual discussion among experts. Objectives Identify the essential factors to consider when making recommendations about DTS. Methods We conducted semi-structured in-depth interviews with experts in assessing evidence and producing guidelines about DTS. Results We interviewed 23 international experts. Although diagnostic test accuracy (DTA) was the factor most commonly considered by organisations when developing recommendations, experts agreed that DTA is never sufficient and may be misleading. Experts identified the following additional essential factors in making decisions about DTS: resource implications, complications, inconclusive results, additional benefits of the test, diagnostic/therapeutic impact, safety, feasibility, ethical, legal, and organisational considerations, patients’ and societies’ values and preferences and the link between the test results and patient important outcomes. Because direct evidence on DTS’s effects on patient outcomes and resource implications is frequently unavailable, most experts agreed that decision analysis and mathematical modelling will be useful, but their opinion varied about the extent of details needed. Discussion Formal decision modelling can be a useful framework for organising the clinical, cost, and preference data relevant to the use of diagnostic tests. Although it requires resources, it is useful for integrating these factors into decision making, identifying evidence gaps, and high priority research areas. Implications Developing guidelines about the use of DTS requires considering factors beyond solely DTA but implementing this demand is challenging. Further development and testing of a framework that can guide this process is needed.
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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 | 0.175 | 0.468 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".