Bibliographic record
Abstract
OBJECTIVE: To review what is known and highlight knowledge gaps regarding Lyme nephritis (LN). DATA SOURCES: Publications identified via PubMed using the keywords "Borrelia burgdorferi," "Borreliosis," "glomerulonephritis," "protein-losing nephropathy," "autoimmunity," and "retriever," and as generated by investigators working in the fields of Borreliosis and immune-mediated glomerulonephritis. HUMAN DATA SYNTHESIS: Postborrelial immune-mediated glomerulonephritis was described recently in 6 people; 3 responded to antimicrobials/steroids, 1 to antimicrobials/angiotensin-converting enzyme inhibitor/warfarin, 1 required hemodialysis but became hemodialysis independent after 5 months and treatment with antimicrobials, steroids, plasmapheresis, immunoglobulin, and 1 did not respond to steroids and angiotensin-converting enzyme inhibitor and still requires hemodialysis. VETERINARY DATA SYNTHESIS: Lyme nephritis is seen in <1-2% of Lyme seropositive dogs, with an average onset at 5-6 years. Labrador and Golden Retrievers are predisposed to this condition. Prior or concurrent lameness is described in 9-28% cases. Historical presentations include acute progressive protein-losing nephropathy with membranoproliferative glomerulonephritis, tubular necrosis/regeneration, and interstitial nephritis, but possibly milder forms exist. Complications include thromboembolic events, hypertension, effusive disease, and oliguric/anuric renal failure. Diagnostic tests help stage disease and rule out other causes. Renal biopsy is advocated early, when intervention may help, and to prove if immune-complex disease exists. Treatment includes standard therapy for protein-losing nephropathy, long-term antimicrobials, and perhaps immunosuppressive therapy. CONCLUSIONS: There is no experimental model of LN to study predisposing factors, pathogenesis, onset, progression, treatment, or prevention. There are no predictive tests to identify the few individuals at highest risk, therefore all seropositive dogs should be screened and monitored for proteinuria. Lyme nephritis mimics other forms of protein-losing nephropathy and sometimes Leptospirosis. Renal biopsy helps show if immune-complex disease exists, but may not prove LN specifically. More studies are warranted on dogs with Lyme-specific immune-complex deposition to evaluate risk factors, understand pathogenesis, variability of expression, and to validate treatment and prevention protocols.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".