An international, population-level initiative to promote healthy lifestyle practices among prostate cancer survivors.
Bibliographic record
Abstract
e287 Background: Growing evidence suggests that specific lifestyle practices (e.g., diet and exercise) may delay cancer progression among prostate cancer survivors (PCS) and restore post-treatment quality of life. However, there are limited efforts to translate this knowledge to sustainable behavior change among PCS at a population level. Methods: TrueNTH is an international partnership to develop innovative evidence-based interventions and care models to improve the physical and mental wellbeing of PCS. Within TrueNTH, four countries (U.S.A., Australia, Canada, U.K.) are developing solutions to improve lifestyle practices among PCS. Australia is implementing face-to-face and telehealth delivery of exercise and dietary programs facilitated by a web-based portal. Canada is implementing group-based exercise and yoga programs combined with a web-based portal to deliver exercise, nutrition and stress-reduction resources for PCS and caregivers. The U.K. is implementing a healthy living pharmacy approach to deliver tailored diet and exercise advice and support to men. The U.S. will implement a scalable web-based portal that reaches PCS, providers and partners and provides personalized exercise prescriptions and dietary advice, interactive guidance, and behavioral support via social media, tele-exercise, and face-to-face communities. Results: TrueNTH initiatives are in various phases of a three-year project. Australia has enrolled 30 of 80 men across 2 sites and via telehealth, and demonstrated acceptance of the portal. Canada has enrolled 30+ men in exercise programs and will soon add 4 sites and launch their portal. The UK has evaluated assessment protocols in 2 sites and will soon add a 3rd site and implement the pharmacy-based program. The U.S. is building the portal and supportive tools and will begin a trial in Fall 2016. Conclusions: Each TrueNTH program shares common goals to improve lifestyle practices among PCS. Common metrics across initiatives will allow for a global comparison of implementation approaches and assessment of impact at a population level. TrueNTH will utilize this initiative to create a lifestyle survivorship program that improves the lives of PCS worldwide.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".