Bibliographic record
Abstract
In the run-up to Evidence Live 2016, we asked health science students, junior doctors, and early career researchers to write about projects or innovative ideas that they have been part of and that deal with some of the conference’s main themes (see box). Below are the top five submissions we received. Their authors will gain free places to attend the conference in Oxford between 22 and 24 June 2016. At the Evidence Live conference, researchers, doctors, and professionals, working with evidence at different stages in the healthcare chain, learn about important issues in healthcare. The programme is designed to showcase the most innovative ideas, processes, and best practices that form the foundations of an evidence based approach. Several sessions focus exclusively on students and junior doctors, including a networking event and leadership showcase. Discounted tickets are still available (http://evidencelive.org/). #### Evidence Live 2016 conference themes #### Holding medical journals to account for publishing trials with switched outcomes ##### Improving the quality of research evidence Aaron Dale, third year graduate entry medical student, University of Oxford, and member of the Centre for Evidence-Based Medicine Outcome Monitoring Project (COMPare) Outcome switching is a major problem in clinical trial reporting that can distort the evidence from which clinical decisions are made. This is when researchers fail to report the outcomes they originally set out to measure and swap them for new outcomes that they didn't initially consider. The best demonstration of why this is a problem is in the webcomic xkcd.1 By leaving out the fact that outcome switching has taken place, journals can represent interventions appearing better than they actually are, which misinforms doctors and risks considerable harm to patients. Outcome switching is highly prevalent …
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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 | 0.052 | 0.115 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.024 | 0.027 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.022 | 0.044 |
| Insufficient payload (model declined to judge) | 0.076 | 0.048 |
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".