Climate change and its influence in nephron mass
Bibliographic record
Abstract
PURPOSE OF REVIEW: The consequences of climate change, including heat and extreme weather events impact kidney function in adults and children. The impacts of climate change on kidney development during gestation and thereby on kidney function later in life have been poorly described. Clinical evidence is summarized to highlight possible associations between climate change and nephron mass. RECENT FINDINGS: Pregnant women are vulnerable to the effects of climate change, being less able to thermoregulate, more sensitive to the effects of dehydration, and more susceptible to infections. Exposure to heat, wildfire smoke, drought, floods and climate-related infections are associated with low birth weight, preterm birth and preeclampsia. These factors are associated with reduced nephron numbers, kidney dysfunction and higher blood pressures in offspring in later life. Exposure to air pollution is associated with higher blood pressures in children and has variable effects on estimated glomerular filtration rate. SUMMARY: Climate change has important impacts on pregnant women and their unborn children. Being born too small or too soon is associated with life-time risk of kidney disease. Climate change may therefore have a dual effect of impacting fetal kidney development and contributing to cumulative postnatal kidney injury. The impact on population kidney health of future generations may be significant.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".