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Diffuse panbronchiolitis in a patient with humoral immunodeficiency successfully treated with erythromycin

2018· article· en· W4313383446 on OpenAlexaff
Stephanie Saridakis, Monica Sandhu, Devi Jhaveri, Robert Hostoffer, Maroun Matta, Haig Tcheurekdjian

Bibliographic record

VenueThe Journal of Immunology · 2018
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsHeritage College
Fundersnot available
KeywordsDiffuse panbronchiolitisErythromycinImmunodeficiencyImmunologyRespiratory tractAntibioticsMedicineHumoral immunityBiologyRespiratory systemImmune systemInternal medicineMicrobiology

Abstract

fetched live from OpenAlex

Abstract Diffuse panbronchiolitis (DPB) is a rare and under diagnosed disease of the respiratory tract that affects the lungs bilaterally in addition to the layers of the respiratory bronchioles. The condition commonly occurs in patients of Japanese ancestry. However, as international migration increases, more cases are arising in the United States. Clinicians rarely consider DPB in the differential when evaluating patients with lung disease as DPB mimics several other pulmonary disorders, especially in patients with humoral immunodeficiencies. We report the first case of DPB in a patient with humoral immunodeficiency that was successfully treated with erythromycin as evidenced by symptom control and follow-up. As our understanding of humoral immunodeficiencies improves, so must the recognition of rare pulmonary diseases such as DPB, which can mimic more common pulmonary disorders seen with humoral immunodeficiencies. Treatment for DPB involves macrolide antibiotics such as erythromycin for a minimum of 6 months duration. We have concluded that DPB can be successfully treated in humoral immunodeficiency with macrolides.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0040.002
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 head0.006
GPT teacher head0.225
Teacher spread0.219 · 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 designCase report
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

Citations0
Published2018
Admission routes1
Has abstractyes

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