Prevention and management of non-communicable disease: the IOC consensus statement, Lausanne 2013
Bibliographic record
Abstract
Morbidity and mortality from preventable, non-communicable chronic disease (NCD) threatens the health of our populations and our economies. The accumulation of vast amounts of scientific knowledge has done little to change this. New and innovative thinking is essential to foster new creative approaches that leverage and integrate evidence through the support of big data, technology and design thinking. The purpose of this paper is to summarise the results of a consensus meeting on NCD prevention sponsored by the IOC in April 2013. Within the context of advocacy for multifaceted systems change, the IOC's focus is to create solutions that gain traction within healthcare systems. The group of participants attending the meeting achieved consensus on a strategy for the prevention and management of chronic disease that includes the following: (1) Focus on behavioural change as the core component of all clinical programmes for the prevention and management of chronic disease. (2) Establish actual centres to design, implement, study and improve preventive programmes for chronic disease. (3) Use human-centred design in the creation of prevention programmes with an inclination to action, rapid prototyping and multiple iterations. (4) Extend the knowledge and skills of Sports and Exercise Medicine (SEM) professionals to build new programmes for the prevention and treatment of chronic disease focused on physical activity, diet and lifestyle. (5) Mobilise resources and leverage networks to scale and distribute programmes of prevention. True innovation lies in the ability to align thinking around these core strategies to ensure successful implementation of NCD prevention and management programmes within healthcare. The IOC and SEM community are in an ideal position to lead this disruptive change. The outcome of the consensus meeting was the creation of the IOC Non-Communicable Diseases ad hoc Working Group charged with the responsibility of moving this agenda forward.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".