Bibliographic record
Abstract
Streptococcal toxic shock syndrome (STSS) is a serious disease caused by invasive group A Streptococcus (GAS) infection. Since the end of 2022, the incidence of scarlet fever and invasive group A Streptococcus (iGAS) has increased in Europe, the USA, and Canada. In March 2024, Japan reported 941 cases of STSS in 2023, which is the highest number of cases since the start of surveillance in 1999. In the Republic of Korea (ROK), scarlet fever, which can be caused by GAS infection, has been designated and managed as a second-class, legally communicable disease. In addition, using the Acute Respiratory Infection Network (AriNet) for sample medical institutions, we have been investigating the status of the epidemic and the characteristics of GAS. The number of scarlet fever cases in the ROK in 2023 was 810, which is a slight increase in numbers from during to after the coronavirus disease 2019 (COVID-19) pandemic but low compared with the numbers before the COVID-19 pandemic. In addition, no increase in GAS numbers was observed using AriNet. Three of the reported cases of scarlet fever since 2000 were suspected STSS cases. In all three cases, rapid hypotension and clinical symptoms, such as renal dysfunction, blood coagulation disorder, and multiple organ failure, were confirmed. Culture tests confirmed GAS. In the third suspected case of STSS, coinfections with influenza and GAS were identified. The Korea Disease Control and Prevention Agency will continue to monitor changes in the incidence of streptococcal infections in the ROK by comprehensively strengthening monitoring by establishing an iGAS monitoring system.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".