Do E-mail Alerts of New Research Increase Knowledge Translation? A “Nephrology Now” Randomized Control Trial
Bibliographic record
Abstract
PURPOSE: As the volume of medical literature increases exponentially, maintaining current clinical practice is becoming more difficult. Multiple, Internet-based journal clubs and alert services have recently emerged. The purpose of this study is to determine whether the use of the e-mail alert service, Nephrology Now, increases knowledge translation regarding current nephrology literature. METHOD: Nephrology Now is a nonprofit, monthly e-mail alert service that highlights clinically relevant articles in nephrology. In 2007-2008, the authors randomized 1,683 subscribers into two different groups receiving select intervention articles, and then they used an online survey to assess both groups on their familiarity with the articles and their acquisition of knowledge. RESULTS: Of the randomized subscribers, 803 (47.7%) completed surveys, and the two groups had a similar number of responses (401 and 402, respectively). The authors noted no differences in baseline characteristics between the two groups. Familiarity increased as a result of the Nephrology Now alerts (0.23 ± 0.087 units on a familiarity scale; 95% confidence interval [CI]: 0.06-0.41; P = .007) especially in physicians (multivariate odds ratio 1.83; P = .0002). No detectable improvement in knowledge occurred (0.03 ± 0.083 units on a knowledge scale; 95% CI: -0.13 to 0.20; P = .687). CONCLUSIONS: An e-mail alert service of new literature improved a component of knowledge translation--familiarity--but not knowledge acquisition in a large, randomized, international population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".