Peer recommendations on how to improve clinical research, and Conference wrap-up
Bibliographic record
Abstract
To promote clinical and patient oriented research, as part of the Second International ALS Conference in Tarrytown, NY, USA, seven pairs of clinicians and scientists were asked to lead discussions with meeting attendees on six major topics (one of which was discussed by two groups); each one the focus of a 90-min Breakout Session. Approximately 25 meeting attendees participated in each session. The Breakout Sessions considered six major themes: 1) Approaches to encourage clinicians to engage in more clinical research to discover the pathogenesis and cause of ALS; 2) Exploring avenues to build more effective partnerships between basic scientists and ALS physicians; 3) Increasing patient interest and commitment to participating in non-trial clinical research; 4) Brainstorming about factors that are most critical to the discovery of the pathogenesis and cause of ALS; 5) Finding ways to incorporate clinical research projects into clinical trials; and 6) Developing state-of-the-art epidemiological studies to solve the mystery of ALS. In this paper, we present the reports from each of the Breakout Sessions; and we provide a wrap-up of the entire conference.
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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 | 0.086 | 0.372 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.025 | 0.014 |
| Insufficient payload (model declined to judge) | 0.389 | 0.310 |
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".