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Record W4243792608 · doi:10.14740/jmc3018w

Bloodless Glomeruli: A Case Report on Scleroderma Renal Crisis

2018· article· en· W4243792608 on OpenAlexvenueno aff
Sunit Tolia, Hassan Kassem, Samira Ahsan

Bibliographic record

VenueJournal of Medical Cases · 2018
Typearticle
Languageen
FieldMedicine
TopicSystemic Sclerosis and Related Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRenal biopsyMicroangiopathic hemolytic anemiaSkin biopsyBiopsyThrombocytopenic purpuraScleroderma (fungus)DermatologyComplicationPopulationThrombotic thrombocytopenic purpuraPresentation (obstetrics)Differential diagnosisKidneySurgeryPathologyInternal medicinePlatelet

Abstract

fetched live from OpenAlex

Scleroderma renal crisis (SRC) is a main complication of systemic sclerosis affecting approximately one-fifth of the patient population with the disease. We present a case of a 38-year-old African American female who initially presented with findings suggestive of thrombotic thrombocytopenic purpura; however, the clinical manifestations, laboratory findings and renal biopsy confirmed the diagnosis of SRC. The case is worth reporting in medical literature because of its unique presentation and to note the findings of the kidney biopsy. SRC can present initially as thrombotic thrombocytopenic purpura; therefore, clinicians need to be more aware of this entity and it should be considered in the differential diagnosis of microangiopathic hemolytic anemia. This case also illustrates that the classic “onion skin appearance” on kidney biopsy in SRC is not always present and that patients can have “bloodless” glomeruli. J Med Cases. 2018;9(7):229-232 doi: https://doi.org/10.14740/jmc3018w

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)0.001
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 score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0030.002
Scholarly communication0.0020.003
Open science0.0010.003
Research integrity0.0040.004
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 head0.046
GPT teacher head0.335
Teacher spread0.289 · 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
Published2018
Admission routes1
Has abstractyes

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