Decision making in family medicine: randomized trial of the effects of the InfoClinique and Trip database search engines.
Bibliographic record
Abstract
OBJECTIVE: To compare the ability of users of 2 medical search engines, InfoClinique and the Trip database, to provide correct answers to clinical questions and to explore the perceived effects of the tools on the clinical decision-making process. DESIGN: Randomized trial. SETTING: Three family medicine units of the family medicine program of the Faculty of Medicine at Laval University in Quebec city, Que. PARTICIPANTS: Fifteen second-year family medicine residents. INTERVENTION: Residents generated 30 structured questions about therapy or preventive treatment (2 questions per resident) based on clinical encounters. Using an Internet platform designed for the trial, each resident answered 20 of these questions (their own 2, plus 18 of the questions formulated by other residents, selected randomly) before and after searching for information with 1 of the 2 search engines. For each question, 5 residents were randomly assigned to begin their search with InfoClinique and 5 with the Trip database. MAIN OUTCOME MEASURES: The ability of residents to provide correct answers to clinical questions using the search engines, as determined by third-party evaluation. After answering each question, participants completed a questionnaire to assess their perception of the engine's effect on the decision-making process in clinical practice. RESULTS: Of 300 possible pairs of answers (1 answer before and 1 after the initial search), 254 (85%) were produced by 14 residents. Of these, 132 (52%) and 122 (48%) pairs of answers concerned questions that had been assigned an initial search with InfoClinique and the Trip database, respectively. Both engines produced an important and similar absolute increase in the proportion of correct answers after searching (26% to 62% for InfoClinique, for an increase of 36%; 24% to 63% for the Trip database, for an increase of 39%; P = .68). For all 30 clinical questions, at least 1 resident produced the correct answer after searching with either search engine. The mean (SD) time of the initial search for each question was 23.5 (7.6) minutes with InfoClinique and 22.3 (7.8) minutes with the Trip database (P = .30). Participants' perceptions of each engine's effect on the decision-making process were very positive and similar for both search engines. CONCLUSION: Family medicine residents' ability to provide correct answers to clinical questions increased dramatically and similarly with the use of both InfoClinique and the Trip database. These tools have strong potential to increase the quality of medical 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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".