CONSORT-EHEALTH: Implementation of a Checklist for Authors and Editors to Improve Reporting of Web-Based and Mobile Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: Randomized trials of web-based and mobile interventions pose very specific issues and challenges. A set of best practices on how to conduct and report such trials was recently summarized in the CONSORT-EHEALTH statement (Consolidated Standards of Reporting Trials of Electronic and Mobile HEalth Applications and onLine TeleHealth), published in August 2011 as draft and in December 2011 as journal article (V1.6.1). The purpose of this presentation is to review the results of the pilot implementation at the Journal of Medical Internet Research (JMIR), a leading eHealth journal, where reporting of trials in accordance with CONSORT-EHEALTH became mandatory in late 2011. METHODS: Authors of all randomized trials submitted to JMIR were asked to complete an electronic questionnaire, which involved copying pertinent manuscript sections into a CONSORT EHEALTH database form, were asked to score the importance of CONSORT EHEALTH items, and were asked to provide narrative feedback on the value of the process. RESULTS: Between August 2011 and November 2012, 67 randomized trials were submitted, of which 61 were intended for publication in JMIR. Authors reported that it took between 1 and 16 hours to complete the checklist including making required changes to their manuscripts. 72% (48/67) of authors reported they made minor changes to the manuscript, 6% (4/67) made major changes. Most authors felt it was a useful process that improved their manuscripts: 63% (42/67) said it improved their manuscript, 13% (9/67) said it did not, 12% (8/67) indicated that it had improved a little. CONCLUSIONS: The CONSORT EHEALTH statement and checklist appeared successful in improving the quality of reporting. The checklist should be endorsed and used by authors and editors of other journals.
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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 | 0.668 | 0.793 |
| Meta-epidemiology (narrow) | 0.008 | 0.011 |
| Meta-epidemiology (broad) | 0.012 | 0.017 |
| Bibliometrics | 0.028 | 0.024 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.010 | 0.012 |
| Research integrity | 0.015 | 0.023 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".