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Record W2971312062 · doi:10.3747/co.26.4859

Pembrolizumab-Induced Obstructive Bronchiolitis in a Patient With Stage IV Non-Small-Cell Lung Cancer

2019· article· en· W2971312062 on OpenAlexaffvenue
A. Blanchard, Nicole Bouchard

Bibliographic record

VenueCurrent Oncology · 2019
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsPembrolizumabMedicinePneumonitisBronchiolitisLung cancerImmune systemImmunologyImmunotherapyInternal medicineLungVirus

Abstract

fetched live from OpenAlex

Objective: Immune checkpoint inhibitors are now a standard of care for the management of many metastatic cancers, including non-small-cell lung cancer. Pembrolizumab, a selective anti-PD-1 monoclonal antibody, augments the host antitumoural response. This hyperactivation of the immune system has side effects, the so-called immune-related adverse effects. The objective of this case report was to review and point out a new pattern of immune checkpoint inhibitor-associated pneumonitis. Case Description: A 69-year-old woman with stage iv non-small-cell lung cancer receiving pembrolizumab presented for increased dyspnea. Pembrolizumab-related obstructive bronchiolitis was diagnosed based on a new severe obstructive disorder, without bronchodilator reversibility, and mosaic attenuation on angiography, without other identifiable causes. Summary: To our knowledge, this is the first description of a case of pembrolizumab-induced obstructive bronchiolitis. Various patterns of immune checkpoint inhibitor-associated lung disease have been described, and bronchiolitis should be included in the differential diagnosis.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0030.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.036
GPT teacher head0.344
Teacher spread0.308 · 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

Citations15
Published2019
Admission routes2
Has abstractyes

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