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Record W1985802190 · doi:10.4236/crcm.2014.35057

Paraneoplastic Leukemoid Reaction in a Patient with Urothelial Carcinoma: A Case Report

2014· article· en· W1985802190 on OpenAlexaff
Aleksi Suo, Tahir Abbas

Bibliographic record

VenueCase Reports in Clinical Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicSoft tissue tumor case studies
Canadian institutionsSaskatchewan Cancer AgencyUniversity of Saskatchewan
Fundersnot available
KeywordsLeukemoid reactionMedicineCystoprostatectomyMalignancyCancerBladder cancerPathologyLymph nodeCystoscopyCarcinomaInternal medicineCystectomy

Abstract

fetched live from OpenAlex

Introduction: Leukemoid reactions in cancer are rare and associated with a poor prognosis. The mechanism driving paraneoplastic leukemoid reactions appears to be gain-of-function granulocyte-colony stimulating factor (G-CSF) secretion by tumour cells. Case Presentation: A 57-year-old male smoker presented with a one-year history of painless frank hematuria and three kilograms weight loss. Cystoscopy revealed a high-grade urothelial carcinoma with sarcomatoid differentiation. The patient was treated by radical cystoprostatectomy, bilateral pelvic lymph node dissection and formation of an ileal conduit. In the absence of bone marrow infiltrations, recurrence of the urothelial carcinoma three months later was associated with a paraneoplastic leukemoid reaction with a white blood cell count peaking at 82.62 × 109/l. Unfortunately, his condition continued to deteriorate and he died shortly thereafter. Conclusion: Monitoring of white blood cell counts in paraneoplastic leukemoid reactions can be a useful indicator of response of the malignancy to chemotherapy or radiotherapy and an indication of relapse after treatment. Paraneoplastic leukemoid reactions are caused by G-CSF secretion by tumour cells and are associated with a poor prognosis. Whether G-CSF signaling plays a role in the aggressive nature of these cancers is currently unknown.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
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 score0.127
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.360
Teacher spread0.317 · 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 teacher head, not a consensus.

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

Citations2
Published2014
Admission routes1
Has abstractyes

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