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Record W2892785587 · doi:10.1016/j.hemonc.2018.09.001

Clinical and histopathological spectrum of toxic erythema of chemotherapy in patients who have undergone allogeneic hematopoietic cell transplantation

2018· article· en· W2892785587 on OpenAlexaff
Manrup Hunjan, Somaira Nowsheen, Alvaro J. Ramos‐Rodriguez, Shahrukh K. Hashmi, Alina G. Bridges, Julia S. Lehman, Rokea A. el‐Azhary

Bibliographic record

VenueHematology/Oncology and Stem Cell Therapy · 2018
Typearticle
Languageen
FieldMedicine
TopicChemotherapy-related skin toxicity
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsHematopoietic cellHematopoietic stem cell transplantationChemotherapyMedicineErythemaTransplantationBroad spectrumHaematopoiesisPathologyImmunologyInternal medicineStem cellBiologyGenetics

Abstract

fetched live from OpenAlex

OBJECTIVE/BACKGROUND: Toxic erythema of chemotherapy (TEC) is a well-recognized adverse cutaneous reaction to chemotherapy. Similar to many skin diseases, the clinical presentations may vary. Our objective is to expand on the typical and atypical clinical and histopathological presentations of TEC. METHODS: Forty patients with a diagnosis of TEC were included from 500 patients who had undergone an allogeneic hematopoietic stem cell transplant. Relevant information and demonstrative photos and pathology were selected. RESULTS: Classic clinical presentations included hand and foot erythema and dysesthesias; atypical presentations included facial involvement, hyperpigmentation, dermatomyositis-like, and erythroderma associated with capillary leak syndrome. CONCLUSION: The diagnosis of TEC should be considered after a correlation of clinical and histological findings in conjunction with a timeline of chemotherapy administration. Suggested criteria for the diagnosis of TEC may be helpful to dermatologists and clinicians when caring for these patients.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.291
Teacher spread0.271 · 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 designObservational
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

Citations18
Published2018
Admission routes1
Has abstractyes

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