Bibliographic record
Abstract
BACKGROUND: In Turkey, an increase of scabies cases was detected since the last quarter of 2019. During the same period, Turkey was also under the influence of the COVID-19 pandemic affecting the whole world. This study aimed to determine the current situation of scabies cases in increasing incidence during COVID-19 pandemic days and to create a scientific resource for the measures to be taken. METHODS: This descriptive study was carried out in July 2020 and the records of patients diagnosed with scabies in health institutions in Kırklareli Province between Jan 2017-June 2020 were retrospectively analyzed. RESULTS: <0.001, CI 95%). The number of scabies cases which increased before the pandemic and reached the epidemic level, decreased dramatically in Mar and Apr 2020. This period was also the period in which the measures taken for the COVID-19 pandemic were most strictly applied. In May and June, the epidemic continued from where it left off. CONCLUSION: COVID-19 pandemic which affects the whole world may create a new opportunity to combat infectious diseases. Not only for COVID-19 but also many infectious diseases, it is necessary to fix the negative socioeconomic and socio-cultural conditions and ensure the sustainability of the new social conditions to be created.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".