Practitioners As Experts: The Influence of Practicing Oncologists “in-the-Field” on Evidence-Based Guideline Development
Bibliographic record
Abstract
PURPOSE: Panels of experts are used to develop clinical practice guidelines (CPGs) intended to be used by practitioners "in-the-field." Therefore, oncologists' participation in CPG development is an important strategy to promote CPG adoption. The purpose of this study was to evaluate the contributions of oncologists in-the-field to evidence-based CPG development using data from Ontario's cancer system. METHODS: CPG development in Ontario includes surveys of oncologists' opinions, using a structured questionnaire, about draft recommendations that were developed from rigorous systematic reviews of evidence prepared by expert panels. Two research assistants reviewed background documents to trace the changes in CPG recommendations from draft to final stage to determine the contribution of oncologists' input to final recommendations. Changes to recommendations were categorized as either substantive (content or tone) or minor (ideas clarification or edits). RESULTS: From 2000 to 2003, 43 CPGs were developed. There were 87 changes to draft recommendations for 31 CPGs, of which 40 changes to 19 CPGs could be attributed to survey input from practicing oncologists. Of the 40 changes, 28 (70%) were judged to be substantive. CONCLUSION: Despite a rigorous evidence-based process for CPG development, practicing oncologists contribute substantially to the final recommendations approved by the expert panel. It is hypothesized that the responsiveness of expert panels to input from oncologists in-the-field will facilitate adoption of CPGs.
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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 | 0.231 | 0.499 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".