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Record W4239939162 · doi:10.1002/9781119028994.ch101

Lyme Nephritis

2018· other· en· W4239939162 on OpenAlexaboutno aff
Meryl P. Littman

Bibliographic record

Venuenot available
Typeother
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineProteinuriaLYMEInterstitial nephritisImmunologyAzotemiaRenal biopsyLyme diseaseLupus nephritisPolyuriaPolydipsiaPathologyGastroenterologyDermatologyInternal medicineBiopsyBorrelia burgdorferiAntibodyKidneyRenal functionDiabetes mellitusEndocrinologyDisease

Abstract

fetched live from OpenAlex

Lyme nephritis, an uncommon serious complication of Lyme borreliosis mostly seen in Labrador and golden retrievers, is a protein-losing nephropathy due to Lyme-specific antigen-antibody complex deposition in renal glomeruli. Lyme nephritis is not necessarily preceded nor accompanied by lameness (Lyme arthritis), but may present emergently, even before azotemia or polyuria/polydipsia exist, due to thromboembolic events, hypertensive target organ damage, nephrotic syndrome, signs of tick-borne or leptospirosis co-infections, or eventual renal failure. Presumptive diagnosis includes serological evidence of exposure while ruling out other causes of proteinuria. Treatment includes antimicrobial therapy and standard treatments for glomerular proteinuria, hypercoagulopathy, hypertension, and renal failure. Immunosuppressive therapy is warranted if immune-complex deposition is documented by cortical renal biopsy, or if non-biopsied cases are rapidly deteriorating despite standard therapy. Without an experimental model or validated stains to prove immune-complexes in glomeruli are Lyme specific, the pathogenesis and best treatment protocol are difficult to study and 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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.004

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 head0.008
GPT teacher head0.230
Teacher spread0.222 · 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 designNot applicable
Domainnot available
GenreOther

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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Same topicVector-borne infectious diseasesFrench-language works237,207