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Record W2142809909 · doi:10.1093/ndt/gfm890

Presence of autoantibodies against tubular and uveal cells in a patient with tubulointerstitial nephritis and uveitis (TINU) syndrome

2007· article· en· W2142809909 on OpenAlexaff
L. Abed, Aïcha Mérouani, Élie Haddad, Geneviève Benoît, Luc L. Oligny, Hervé Sartelet

Bibliographic record

VenueNephrology Dialysis Transplantation · 2007
Typearticle
Languageen
FieldMedicine
TopicNephrotoxicity and Medicinal Plants
Canadian institutionsUniversité de MontréalCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsMedicineUveitisAutoantibodyNephritisInterstitial nephritisImmunologyDermatologyPathologyInternal medicineKidneyAntibody

Abstract

fetched live from OpenAlex

Tubulointerstitial nephritis and uveitis (TINU) syndrome is characterized by acute tubulointerstitial nephritis with a favourable course and chronic recurrent uveitis. Since the first description in 1975 [ 1 ], more than 150 cases have been described in the literature [ 2 ]. Most of the patients with TINU syndrome are adolescents and young women, with a median age of onset of 15 (range 9 to 74) years. Renal tubulointerstitial infiltrates are primarily composed of activated lymphocytes, among which the helper/inducer T-cell subset is reported to be predominant [ 3,4 ]. In addition, TINU syndrome can be associated with granuloma in kidney or in another localization like bone marrow [ 1 , 5–7 ]. The pathogenesis of TINU syndrome remains unclear, but cell-mediated immunity, in particular delayed-type hypersensitivity, could play a large role in this disorder [ 8 ]. In addition, some studies suggest that uveitis and tubulointerstitial nephritis have a common immunological pathogenesis and so it was postulated that there may be a common antigenicity between renal and ocular tissues [ 3 , 9 ].

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)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.006
GPT teacher head0.225
Teacher spread0.219 · 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

Citations64
Published2007
Admission routes1
Has abstractyes

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