Management and outcome assessment of pregnancy-related acute kidney injury in Western India: a single centred, prospective, observational study
Bibliographic record
Abstract
Background: Pregnancy-related acute kidney injury (PRAKI) remains a large public health problem, with decreasing incidences in developing countries like India. However, some single centred studies from United States and Canada revealed an increasing incidence of PRAKI. This increase could be due to higher rates of hypertensive disorders of pregnancy.Methods: To assess the management and outcome of PRAKI. In this prospective, observational study, total 1021 cases of acute renal failure were observed.Results: 96 (9.4%) were of obstetric origin and enrolled as per inclusion criteria. Regarding management of PRAKI, 78 out of 96 (81.25%) required haemodialysis. 67 (69.79%) among them were managed with intermittent haemodialysis (IHD) while 10 (10.41%) who had hypotension at presentation were dialysed with slow, low efficiency dialysis (SLED). Continuous renal replacement therapy (CRRT) was done in 1 (10.4%) patient. Maternal mortality in this PRAKI study was 19 of 96 patients (19.79%). Sepsis accounted for 52.63% of deaths. Foetal death was observed in 58 out of 96 patients (60.41%) comprising of intrauterine death in 55 (55.29%) and abortion in 3 (3.13%) patients. 38 of 96 (39.58%) patients gave birth to live born child out of which 27 were at full term and 11 were preterm.Conclusions: In order to avoid further increase in PRAKI in India, treating obstetrician should remain aware of management and outcome of PRAKI. The better awareness of diagnosis and management protocols will ultimately lead to further reduction in prevalence of PRAKI in our country.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 0.000 |
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".