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Record W4200569709 · doi:10.1111/pan.14382

Pediatric lower respiratory tract infection: Considerations for the anesthesiologist

2021· review· en· W4200569709 on OpenAlexaff
Ekta Rai, Rashid Alaraimi, Is’haq Al Aamri

Bibliographic record

VenuePediatric Anesthesia · 2021
Typereview
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsMedicineRespiratory tract infectionsRespiratory tractPneumoniaBronchiolitisCroupLower respiratory tract infectionMeaslesIntensive care medicineRespiratory diseaseEtiologyDiphtheriaVaccinationRespiratory systemPediatricsImmunologyInternal medicineLung

Abstract

fetched live from OpenAlex

Neonatal and childhood infectious diseases continue to be a global health problem. Acute respiratory tract infections are typically classified as upper respiratory tract infection and lower respiratory tract infections. The most common lower respiratory infections in childhood are pneumonia and bronchiolitis. Vaccination against measles, diphtheria, pertussis, Haemophilus influenzae, pneumococcus, and influenza resulted in a significant reduction in the incidence of acute respiratory tract infection globally. Though the global burden of the disease has decreased, the mortality rates still are higher in developing countries. Patients with severe lower respiratory tract infections and their complications are often evaluated for elective or emergency procedures. In this review article, the authors aim to discuss the etiology, pathogenesis, preoperative evaluation of lower respiratory tract infections, and the anesthesia implications pertinent to the practice of anesthesia.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.109
GPT teacher head0.364
Teacher spread0.255 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations6
Published2021
Admission routes1
Has abstractyes

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