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Record W2338350421 · doi:10.1093/ofid/ofv133.396

Hospital Admissions Associated to Respiratory Viruses, 2014–2015 Season: Valencia Hospital Network for the Study of Influenza and Other Respiratory Viruses (Spain)

2015· article· en· W2338350421 on OpenAlexaboutno aff
Joan Puig‐Barberà, Ainara Mira‐Iglesias, Miguel Tortajada‐Girbés, F. Xavier López‐Labrador, Ángel Belenguer‐Varea, Mario Carballido‐Fernández, Ramón Limón-Ramírez, Empar Carbonell-Franco, Joan Mollar‐Maseres, Maria Del Carmen Otero-Reigada, Germán Schwarz‐Chavarri, José Tuells-Hernández, Vicente Gil-Guillén

Bibliographic record

VenueOpen Forum Infectious Diseases · 2015
Typearticle
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRespiratory systemInfluenza seasonEmergency medicineRespiratory illnessVirologyPediatricsVirusInternal medicineInfluenza vaccine

Abstract

fetched live from OpenAlex

Background. Burden of disease associated with respiratory viruses (RV) is poorly defined. The aim of this study was to describe the incidence of admissions with RV, by age and virus. Methods. Prospective active surveillance study of consecutive admissions with RV confirmed infection in ten hospitals (catchment area 2,351,526 inhabitants) in the Valencia region (Spain). We identified all patients admitted with symptoms possibly associated with a recent viral infection. After consent, we obtained nasopharyngeal swabs that were tested by RT-PCR. We obtained population data from the health care Population Information System. Results. From epidemiological week 45-2014 to 13-2015, we ascertained 9363 eligible admissions, 87% consented to be included, 4429 (47%) met all inclusion criteria, valid samples were available from 4423 (47%). Overall, 1689 (38%) were positive for a RV: 741 (44%) influenza, 363 (21%) rhino/enterovirus, 226 (13%) respiratory syncytial virus, 134 (8%), coronavirus, 115 metapneumovirus (7%), 79 (5%) mixed infections and 1% or less for bocavirus, parainfluenza or adenovirus. Admission rates with a RV were 1575 per 100,000 inhabitants <1 years old, 210 in those 1 to 4 years old, and 232 per 100,000 in patients aged ≥65 (figure 1). A(H3N2) was predominant (613, 83% of all influenza) with admission rates of 98 per 100,000 inhabitants <1 years old and 57, 139 and 252 admissions per 100,000 in patients 65-74, 75-84 and ≥85 years old, respectively. RSV admission rates were 679 per 100,000 inhabitants <1, 50 per 100,000 in those 1 to 4 years old, and 6, 15 and 26 per 100,000 in patients aged 65-74, 75-84 and ≥85, respectively. Rhino/enterovirus were associated with 326 admissions per 100,000 inhabitants <1 year old, 55 per 100,000 1 to 4, and 21, 53 and 87 per 100,000 in patients aged 65-74, 75-84 and ≥85, respectively Figure. Age-specific admission rates. Conclusion. The greatest burden of disease due to admissions with respiratory virus infection was observed mainly in children under one year of age with 1.6 % children in their first year of life being admitted at least once. The incidence of admissions in the group of 1 to 4 years of age was similar to that experienced in the group aged 65 or more. Disclosures. J. Puig-Barberà, Sanofi Pasteur Institution: funding for the study, Research support; A. Mira-Iglesias, Sanofi Pasteur Institution: funding for the study, Research support; M. Tortajada-Girbés, Sanofi Pasteur Institution: funding for the study, Research support; F. X. López-Labrador, Sanofi Pasteur Institution: funding for the study, Research support; Belenguer-Varea, Sanofi Pasteur Institution: funding for the study, Research support; C. Carratalà-Munuera, Sanofi Pasteur Institution: funding for the study, Research support; M. Carballido-Fernández, Sanofi Pasteur Institution: funding for the study, Research support; R. Limón-Ramírez, Sanofi Pasteur Institution: funding for the study, Research support; E. Carbonell-Franco, Sanofi Pasteur Institution: funding for the study, Research support; J. Mollar-Maseres, Sanofi Pasteur Institution: funding for the study, Research support; M. D. C. Otero-Reigada, Sanofi Pasteur Institution: funding for the study, Research support; G. Schwarz-Chavarri, Sanofi Pasteur Institution: funding for the study, Research support; J. Tuells-Hernández, Sanofi Pasteur Institution: funding for the study, Research support; V. Gil-Guillén, Sanofi Pasteur Institution: funding for the study, Research support.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.095
GPT teacher head0.412
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2015
Admission routes1
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