Research Milestone Forecasting in Cystic Fibrosis
Bibliographic record
Abstract
ABSTRACT:Background: Cystic fibrosis (CF) has seen important breakthroughs in the last few years. Physicians, experts, and patients anticipate further treatment advances in the coming years.Methods: In October 2021, CF experts in the US and Canada were asked to provide forecasts about 8 milestones in cystic fibrosis research in an online survey (Canadian experts provided forecasts about two additional milestones about healthcare system outcomes). CF experts were identified using lists of CF clinics available from major CF advocacy groups in the US and Canada. We randomized experts into two different experimental arms, comparing two different forecast elicitation approaches. Results: 81 experts completed the survey. Forecasts were not particularly optimistic about near term breakthroughs, though many events, including approval of anti-inflammatory drugs and approval of an amplifier drug were viewed as having at least a 40% chance of happening in the next 6 years. Experts were still less sanguine about other events, including FDA approval of effective antibiotics for B. Cepacia, FDA approval of a therapy for those with stop mutations on both alleles and a Phase II trial testing a gene therapy (25%, 27%, and 27% probability of not occurring at all within the next 10 years). Overall, both elicitation methods produced very similar forecasts. Conclusions: Experts regard important advances like genetic therapy as unlikely to occur within the decade. However, experts are very optimistic about healthcare system access to new treatments, at least in Canada.
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 | 0.019 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".