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Record W1598335803 · doi:10.1111/petr.12553

Respiratory syncytial virus infections in pediatric transplant recipients: A Canadian Paediatric Surveillance Program study

2015· article· en· W1598335803 on OpenAlexafffundabout
Joan Robinson, Danielle Grenier, Ian MacLusky, Upton Allen

Bibliographic record

VenuePediatric Transplantation · 2015
Typearticle
Languageen
FieldMedicine
TopicRespiratory viral infections research
Canadian institutionsSickKids FoundationUniversity of TorontoChildren's Hospital of Eastern OntarioUniversity of OttawaCanadian Paediatric SocietyHospital for Sick ChildrenStollery Children's HospitalUniversity of Alberta
FundersPublic Health Agency of Canada
KeywordsMedicineIncidence (geometry)PediatricsMechanical ventilationTransplantationRespiratory systemIntensive care medicineEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

The incidence and spectrum of severity of RSV infections in SOT or HSCT recipients is not known. From September 2010 through August 2013, pediatricians were surveyed monthly by the CPSP for SOT or HSCT recipients with RSV infection within two yr post-transplant. There were 24 completed case report forms that fit the inclusion criteria (10 HSCT and 14 SOT recipients). Six of 24 cases (25%) remained outpatients, and 11 (46%) were managed on an inpatient ward, while seven (29%) required intensive care of which five required mechanical ventilation and two died of RSV infection. Ten of 23 cases (43%) were nosocomial with these data not recorded for one case. Many transplant recipients recover uneventfully from RSV infection in the first two yr post-transplant. However, severe disease and death also occur. Larger studies are required to establish risk factors for poor outcomes. Prevention of nosocomial RSV should be a priority in transplant recipients.

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 score0.043
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0010.000
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 head0.052
GPT teacher head0.353
Teacher spread0.300 · 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".

Quick stats

Citations20
Published2015
Admission routes3
Has abstractyes

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