Glucose management for exercise using continuous glucose monitoring: should sex and prandial state be additional considerations? Reply to Yardley JE and Sigal RJ [letter]
Bibliographic record
Abstract
Oral diseases are a significant global health problem across all countries and populations. With about 3.5 billion cases (2017), more people are affected than by any other disease group. The main oral diseases comprise tooth decay of permanent and deciduous teeth, severe periodontal disease, and oral and lip cancer. With a largely unchanged high global prevalence, but significantly growing population sizes, the pressure on health systems is increasing, particularly in low- and middle-income countries.Nonetheless, in many countries oral health has insufficient priority as a key health topic, including the global health policy discourse of German and international stakeholders. One of the fundamental challenges is ensuring universal and equitable access to basic oral healthcare services for all and without financial hardship (Universal Health Coverage).This paper provides an introductory overview of the global trends for the main oral diseases, which are generally characterized by stark inequalities. Opportunities for improving the situation through population-wide risk reduction and preventive approaches, access to oral healthcare, and policy options are highlighted. In addition, a range of relevant global (oral) health topics with potential for tangible change are discussed. Lastly, the reform areas of the Lancet Series on Oral Health from 2019 are presented and recommendations for the German and international global health policy discourse are provided.
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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.039 | 0.043 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".