Динамика охвата профилактическими осмотрами населения врачами-стоматологами
Bibliographic record
Abstract
The aim of the article: to assess the dynamics of the population coverage of preventive inspections dentists. Material and methods: analytical, statistical, and direct observation. Results. Increase in the number of patients receiving artificial limbs, for example, in the North Caucasus Federal District (+21.8% from 2008 to 2009) And Far East (-30.1 % over the same period). Given that in the North Caucasus Federal District smallest number of patients received pros-theses, such a growth indicator may be due to the volume of supply in the region, taking into account the needs of the population. The sharp decline in the availability of prosthetic patients Far East against the background of dissonance between sanitized and in need of rehabilitation patients with sufficient personnel potential evidence of regional problems in dental care. Even more interesting is the distribution of the proportion of patients who received free dentures. In the North Caucasus Federal District, along with an increase in the total number of patients who received artificial limbs, sharply reduced the proportion of patients who received them free of charge (-64.9% from 2008 to 2009). In a number of countries have not produced any free prosthetics. Conclusion. Reduced the number of patients who received pros-theses, especially for free. The most unfavorable situation is observed in the North Caucasus Federal District (-64.9%), mainly due to the Chechen Republic and the Republic of Dagestan, where the figure is zero. The current situation in the field of dental care, requires a differentiated approach and to adequately address problems from a regional perspective.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".