What we do and do not know about women and kidney diseases: Questions unanswered and answers unquestioned: Reflection on world kidney day and international women's day
Bibliographic record
Abstract
What We Know and Do Not KnowPregnancy is a unique challenge and is a major cause of acute kidney injury (AKI) in women of childbearing age; AKI and preeclampsia (PE) may lead to subsequent CKD, but the entity of the risk is not completely known. [2][3]4][5] CKD has a negative effect on pregnancy even at very early stages. [6,7]The risks increase with CKD progression, thus posing potentially challenging ethical issues around conception and maintaining of pregnancies. [6][7][8] We do know that PE increases the probability of hypertension and CKD in later years, but we have not evaluated a surveillance or reno-protective strategy to determine if progressive loss of kidney function can be attenuated. [9][10]1][12] Specific systemic conditions such as systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and systemic scleroderma (SS) are more likely to affect women than men.We do not know the relative contribution of these acute and chronic conditions on progression to end-stage renal disease (ESRD) in women.
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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 | 0.024 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.011 | 0.030 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.021 | 0.057 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".