662 FOCUSING RESEARCH TO ENSURE IMPACT
Bibliographic record
Abstract
Of the burden of non-communicable diseases (NCDs), 80% is borne by low- and middle-income countries (LMIC) and by people below the age of 65. To provide evidence to enable policy makers to implement cost-effective ways of preventing and controlling non-communicable chronic diseases globally, major health and medical research funding agencies from seven countries formed the Global Alliance for Chronic Disease (GACD) in 2008. The aim of the GACD is to develop the evidence base needed to guide policy and identify best practices for fighting chronic diseases, thereby contributing to a sustainable and significant reduction of illness, disability and premature death around the world. GACD chose hypertension control in LMIC as its first research priority. In 2011, GACD members from Australia (NHMRC), Canada (CIHR), the UK (MRC), and the USA (NHLBI) opened coordinated targeted calls for hypertension research, to support collaborative research projects. Rigorous scientific evaluations of the applications from investigators from developed countries and LMIC were carried out by the four funding agencies and a wide range of innovative projects have been identified. A joint announcement of successful research applications will be made in June 2012. GACD will convene funded investigators to create a network of hypertension researchers. This research, covering many nations of the world, should lead to practical ways of meeting the targets identified by WHO following the United Nations’ meeting on NCD in September 2011. GACD is identifying priorities for future targeted research calls. Experiences to date, intended outcomes and future strategies will be presented.
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 | 0.278 | 0.419 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.023 | 0.022 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.014 | 0.014 |
| Insufficient payload (model declined to judge) | 0.044 | 0.025 |
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".