Using a systematic review in clinical decision making: a pilot parallel, randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Evidence suggests that systematic reviews are used infrequently by physicians in clinical decision-making. One proposed solution is to create filtered resources so that information is validated and refined in order to be read quickly. Two shortened systematic review formats were developed to enhance their use in clinical decision-making. METHODS: To prepare for a full-scale trial, we conducted a pilot study to test methods and procedures in order to refine the processes. A recruitment email was sent to physicians practicing full- or part-time in family medicine or general internal medicine. The pilot study took place in an online environment and eligible physicians were randomized to one of the systematic review formats (shortened or full-length) and instructed to read the document. Participants were asked to provide the clinical bottom line and apply the information presented to a clinical scenario. Participants' answers were evaluated independently by two investigators against "gold standard" answers prepared by an expert panel. RESULTS: Fifty-six clinicians completed the pilot study within a 2-month period with a response rate of 4.3 %. Agreement between investigators in assessing participants' answers was determined by calculating a kappa statistic. Two questions were assessed separately, and a kappa statistic was calculated at 1.00 (100 % agreement) for each. CONCLUSIONS: Agreement between investigators in assessing participants' answers is satisfactory. Although recruitment for the pilot study was completed in a reasonable time-frame, response rates were low and will require large numbers of contacts. The results indicate that conducting a full-scale trial is feasible. TRIAL REGISTRATION: ClinicalTrials.gov NCT02414360 .
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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 | 0.128 | 0.177 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".