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Pyomyositis After Chemotherapy for Breast Cancer

2000· article· en· W2328073063 on OpenAlexaff
Bruce Keith, Vivien Bramwell

Bibliographic record

VenueAmerican Journal of Clinical Oncology · 2000
Typearticle
Languageen
FieldMedicine
TopicInfectious Diseases and Tuberculosis
Canadian institutionsOttawa Regional Cancer Foundation
Fundersnot available
KeywordsPyomyositisMedicineMyositisMalignancyChemotherapyVenous thrombosisBreast cancerComplicationThrombosisSurgeryCancerInternal medicineAbscess

Abstract

fetched live from OpenAlex

Pyomyositis is a rare complication of chemotherapy. A 47-year-old woman with metastatic breast cancer, in whom pyomyositis developed after chemotherapy, is described. It was difficult to differentiate between pyomyositis and deep venous thrombosis early in her admission. Pyomyositis should be considered part of the differential diagnosis of deep venous thrombosis. This infection, after chemotherapy, usually is considered to be caused by neutropenia or immunodeficiency secondary to the cancer, or both. It is postulated that subclinical myopathy, secondary to the malignancy or drugs used in treating the malignancy, or both, may also predispose to pyomyositis.

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)0.003
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.434
Teacher spread0.412 · 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

Citations13
Published2000
Admission routes1
Has abstractyes

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Same venueAmerican Journal of Clinical OncologySame topicInfectious Diseases and TuberculosisFrench-language works237,207