Bibliographic record
Abstract
Abundant evidence supports the association between low birth weight (LBW) and renal dysfunction in humans. Anatomic measurements of infants, children, and adults show significant inverse correlation between LBW and nephron number. Nephron numbers are also lower in individuals with hypertension compared with normotension among white and Australian Aboriginal populations. The relationship between nephron number and hypertension among black individuals is still unclear, although the high incidence of LBW predicts low nephron number in this population as well. LBW, a surrogate for low nephron number, also associates with increasing BP from childhood to adulthood and increasing risk for chronic kidney disease in later life. Because nephron numbers can be counted only postmortem, surrogate markers such as birth weight, prematurity, adult height, reduced renal size, and glomerulomegaly are potentially useful for risk stratification, for example, during living-donor assessment. Because early postnatal growth also affects subsequent risk for higher BP or reduced renal function, postnatal nutrition, a potentially modifiable factor, in addition to intrauterine effects, has significant influence on long-term cardiovascular and renal health.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".