UpToDate versus DynaMed: a cross-sectional study comparing the speed and accuracy of two point-of-care information tools
Bibliographic record
Abstract
OBJECTIVE: To compare the accuracy, time to answer, user confidence, and user satisfaction between UpToDate and DynaMed (formerly DynaMed Plus), which are two popular point-of-care information tools. METHODS: A crossover study was conducted with medical residents in obstetrics and gynecology and family medicine at the University of Toronto in order to compare the speed and accuracy with which they retrieved answers to clinical questions using UpToDate and DynaMed. Experiments took place between February 2017 and December 2019. Following a short tutorial on how to use each tool and completion of a background survey, participants attempted to find answers to two clinical questions in each tool. Time to answer each question, the chosen answer, confidence score, and satisfaction score were recorded for each clinical question. RESULTS: A total of 57 residents took part in the experiment, including 32 from family medicine and 25 from obstetrics and gynecology. Accuracy in clinical answers was equal between UpToDate (average 1.35 out of 2) and DynaMed (average 1.36 out of 2). However, time to answer was 2.5 minutes faster in UpToDate compared to DynaMed. Participants were also more confident and satisfied with their answers in UpToDate compared to DynaMed. CONCLUSIONS: Despite a preference for UpToDate and a higher confidence in responses, the accuracy of clinical answers in UpToDate was equal to those in DynaMed. Previous exposure to UpToDate likely played a major role in participants' preferences. More research in this area is recommended.
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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 | 0.010 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".