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Record W2982014413 · doi:10.3747/co.26.5007

Gemcitabine-Induced Pseudocellulitis: A Case Report and Review of the Literature

2019· review· en· W2982014413 on OpenAlexaffvenue
Herman Bami, Chelain R. Goodman, Gabriel Boldt, M. Vincent

Bibliographic record

VenueCurrent Oncology · 2019
Typereview
Languageen
FieldMedicine
TopicChemotherapy-related skin toxicity
Canadian institutionsCancer Care Ontario
Fundersnot available
KeywordsMedicineGemcitabineRashErythemaVenous stasisDermatologyEmergency departmentMedical historyAdenocarcinomaErlotinibSurgeryInternal medicineCancerChemotherapy

Abstract

fetched live from OpenAlex

Gemcitabine is a chemotherapeutic agent used in a wide variety of solid tumours. Known side effects include a dose-limiting myelosuppressive toxicity, mild rash, and radiation-dependent dermatitis. Rarely, localized inflammation in the form of pseudocellulitis has also been observed. We present the case of a 77-year-old woman with a history of a Whipple procedure for pancreatic adenocarcinoma who presented to the emergency department after the start of gemcitabine therapy with increased erythema, swelling, and tenderness in her lower legs. Relevant past medical history included peripheral vascular disease, dyslipidemia, and hypertension. A diagnosis of gemcitabine-induced pseudocellulitis aggravated by venous stasis was confirmed after an extensive workup. This case report and the literature review describe this rare reaction, highlighting the need for increased recognition to avoid unnecessary therapeutic intervention.

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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.006
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
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 head0.151
GPT teacher head0.456
Teacher spread0.305 · 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
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

Citations10
Published2019
Admission routes2
Has abstractyes

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