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Record W4403431163 · doi:10.1080/1120009x.2024.2416348

Case report: hypertrophic osteoarthropathy improves with immune checkpoint inhibitor therapy

2024· article· en· W4403431163 on OpenAlexaff
David Moon, Quincy S. Chu, Carrie Ye

Bibliographic record

VenueJournal of Chemotherapy · 2024
Typearticle
Languageen
FieldMedicine
TopicHypertrophic osteoarthropathy and related conditions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsHypertrophic osteoarthropathyMedicineImmune systemDermatologyImmunology

Abstract

fetched live from OpenAlex

We report a case of a 66 year-old male with recurrent stage IIIA non-small cell lung cancer (NSCLC) and no prior arthritis or bone disease who developed hypertrophic osteoarthropathy (HOA) prior to immunotherapy treatment. Approximately one month after the first durvalumab infusion and without other interventions for symptom management, the patient reported improvements to his hand pain, with complete resolution of symptoms after five durvalumab treatments. Repeat x-ray after nine cycles of durvalumab showed decreased periosteal thickening of the phalanges bilaterally. He had no evidence of recurrent NSCLC based on serial computed tomography one year after durvalumab initiation. To our knowledge, there are no documented reports on the isolated effect of immune-checkpoint inhibitor (ICI) therapy on HOA. This case suggests that durvalumab may have a positive role in the management of HOA in NSCLC patients. Further research is needed to better understand the interaction of ICIs, HOA and other paraneoplastic syndromes.

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 score0.008

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)0.0020.001

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 head0.011
GPT teacher head0.251
Teacher spread0.240 · 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
Published2024
Admission routes1
Has abstractyes

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