Perspectives from NHLBI Global Health Think Tank Meeting for Late Stage (T4) Translation Research
Bibliographic record
Abstract
Almost three-quarters (74%) of all the noncommunicable disease burden is found within low- and middle-income countries. In September 2014, the National Heart, Lung, and Blood Institute held a Global Health Think Tank meeting to obtain expert advice and recommendations for addressing compelling scientific questions for late stage (T4) research-research that studies implementation strategies for proven effective interventions-to inform and guide the National Heart, Lung, and Blood Institute's global health research and training efforts. Major themes emerged in two broad categories: 1) developing research capacity; and 2) efficiently defining compelling scientific questions within the local context. Compelling scientific questions included how to deliver inexpensive, scalable, and sustainable interventions using alternative health delivery models that leverage existing human capital, technologies and therapeutics, and entrepreneurial strategies. These broad themes provide perspectives that inform an overarching strategy needed to reduce the heart, lung, blood, and sleep disorders disease burden and global health disparities.
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 | 0.075 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.012 | 0.019 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".