A case series of 72 neonates with renal vein thrombosis
Bibliographic record
Abstract
Neonatal renal vein thrombosis (RVT) is a well-recognized clinical entity which is associated with serious morbidity. However, current information regarding RVT has been restricted to case reports and small case series. In this study, it was our objective to describe patient demographics, clinical presentation, location and risk factors of RVT. For our study design, we looked at a case series of 72 neonates with RVT referred to the 1-800-NO-CLOTS consultation service between 9/1996 and 8/2001. Data on age, gender, associated conditions, prothrombotic disorders, family history, location of the thrombosis, diagnostic techniques, and treatment were prospectively recorded using a standardized form. Our results show that RVT affected males (65%, CI 52-76%) significantly more often than females (35%, CI 24-48%). Median age at presentation was 2 days (0-21 days). RVT was unilateral in 72% (left side: 67%,CI 49-81%; right side: 33%, CI 19-51%), and bilateral in 28%. The majority (83%) had at least one associated condition: Prematurity (54%), central venous lines (17%), a diabetic mother (13%), asphyxia (6%), infections (6%). Prothrombotic testing was performed in 21 neonates. Activated protein C resistance was found in 8 children (38%), other defects in three. This is the largest case series of neonatal RVT to date. Data from the study show that i) male infants are affected twice as often as females and ii) there appears to be a left-sided predominance of neonatal RVT. Neonatal RVT is only infrequently associated with the presence of a catheter as compared to thrombosis at other sites. The majority of infants have associated conditions with prematurity being most frequent. A small subset of neonates were screened for prothrombotic abnormalities and 50% of the children screened were positive.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".