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Record W2937729871 · doi:10.1093/pch/pxy170

Kidney and inferior vena cava abnormalities with leg thromboses (KILT) syndrome: A case report and literature review

2019· article· en· W2937729871 on OpenAlexaffabout
Prita Rughani, Frances Yeung, Camilla Raya Halgren, Michaela Cada, Sarah Schwartz

Bibliographic record

VenuePaediatrics & Child Health · 2019
Typearticle
Languageen
FieldMedicine
TopicVascular anomalies and interventions
Canadian institutionsHospital for Sick ChildrenVictoria Hospital
Fundersnot available
KeywordsMedicineInferior vena cavaAgenesisThrombosisVenous thrombosisThrombusHypoplasiaVenous thromboembolismPopulationIncidence (geometry)PediatricsPulmonary hypoplasiaSurgeryPregnancyFetus

Abstract

fetched live from OpenAlex

Venous thromboembolism (VTE) is now increasingly recognized within paediatrics. A Canadian VTE registry has estimated the incidence as 0.7 to 1.0 per 100,000 population, with a peak in infancy and adolescence. Congenital inferior vena cava agenesis (IVCA) is an important risk factor that may be unfamiliar to paediatricians. Several case reports have since described an association between IVCA, VTE, and renal hypoplasia, which has been referred to as KILT syndrome (Kidney and IVC abnormalities with Leg Thromboses). We describe the first reported paediatric case of KILT syndrome in Canada. In any young patient presenting with a spontaneous DVT, particularly, if it is bilateral in nature with no co-existing risk factors for thrombus formation, we recommend investigating for the possibility of an underlying congenital vena cava anomaly. The use of prolonged anticoagulant therapy is supported by the inherent life-long risk of recurrent thrombosis associated with IVC anomalies.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.007
Science and technology studies0.0010.002
Scholarly communication0.0020.003
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.009
GPT teacher head0.274
Teacher spread0.265 · 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

Citations6
Published2019
Admission routes2
Has abstractyes

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