Bibliographic record
Abstract
In a hospital in Morocco, cancer patients must wait 2 or 3 days for a bed to open up. In a Guatemalan health center, two harried clinicians juggle all of the more than 400 new cases of leukemia each year. In some places in India, 60%–70% of cancer patients are turned away from hospitals because of a lack of medical resources. “Clinical trials? Wouldn't [providing] soap be a better place to start?” Ronald Barr , M.D., of McMaster University in Canada, said of conducting clinical trials in developing countries, only half joking. He remembers handing out blocks of soap to doctors in Kenya, who gave the precious bars to parents as an incentive not to abandon their sick children in hospitals. “There are huge fundamental challenges that need to be addressed before you can think about doing trials,” he added. Researchers at pharmaceutical companies and academic institutions and organizations are addressing these challenges head-on as interest grows in conducting clinical trials in developing regions, whether for humanitarian reasons, furthering scientific knowledge, or economic savings. However, opinions differ as to the types of trials needed and how such trials should be conducted.
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 | 0.231 | 0.267 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.027 | 0.021 |
| Open science | 0.006 | 0.017 |
| Research integrity | 0.017 | 0.034 |
| Insufficient payload (model declined to judge) | 0.036 | 0.023 |
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".