Características de los casos y contactos de COVID-19 identificados en un área de Madrid durante el inicio de la desescalada
Bibliographic record
Abstract
Background: Deescalation began in May 2020 increases \nsocial interaction, which has an influence on \nCOVID-19 epidemiological surveillance. The aim of this \nstudy was the characterization of COVID-19 cases detected \nduring this period. \nMethods: We analyzed certain variables of interest \ncoming from the epidemiological surveys carried out in \nan area of Madrid during May 2020, and stratified the \nresults depending on its temporal relation with the deescalation. \nPrevalence for each category of response and \naverage duration in minutes of the telephonic call were \ncalculated. Confidence intervals were estimated at 95%. \nResults: We included 167 cases, being 30.5% of them \nincident and 49.1% prevalent. The main source of infection \nwas home (38.0%; CI 95% 31.4-46.2). Regarding healthcare \nand social care workers, the main source of infection was \nworkplace (93.0%; 85.4-100). Average number of close contacts \nper case was 2.0 (1.8-2.2), being 1.5 (1.0-2.0) among \npre-deescalation incident cases and 2.4 (1.8-3.0) among those \npost-deescalation. Average duration of each survey was \n35.9 minutes (32.2-38.9), being 32.1 (24.4-39.8) among predeescalation \nincident cases and 37.0 (29.6-44.4) among those \npost-deescalation. Most of the contacts were household, \nboth before and after beginning of deescalation. \nConclusions: Home is the most prevalent place for \nthe acquisition of the infection among general population, \nwhile workplace is the most prevalent among healthcare \nand social care workers. The initial phase of deescalation \ndo not represents a change regarding sources of infection, \nbut it may increase the number of close contacts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".