Characterization of acute respiratory infections behavior. Cienfuegos Province. First quarter 2020
Bibliographic record
Abstract
Foundation: due to the current pandemic of COVID 19, it is important to know the categorizing essentialities of acute respiratory infections in general for future studies in which COVID 19 will constitute a category of the taxonomy of those to be taken into account. Objective: to characterize acute respiratory infections in the Cienfuegos province from January 1 to March 31, 2020. Methods: the population studied was the population of the Cienfuegos province. The main study variables considered were: age groups, municipalities of residence, demand for medical consultations, statistical weeks, rates temporal variation, their trend, severity of clinical evolution, admissions and deaths from severe respiratory infections, outbreaks and epidemiological surveillance. Statistical methods and techniques used were, from descriptive statistics: absolute and relative frequencies, means, rates, increases and decreases in rates, the endemic corridor and its trend. Results: the characterizing essentialities were: its highest incidence in children under five years of age and adults 60 years and over; the leading cause of death was from community-acquired pneumonia; the identified circulating viruses were respiratory syncytial, parainfluenza, unsubtyped influenza A, and coronavirus. The trend is upward. Conclusions: the behavior of acute respiratory infections was within the expected parameters, except that from week eleven there was an increase in medical care, which could be related to the inquiring and surveillance actions.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".