Additional file 1 of Changing epidemiology of acute kidney injury in critically ill patients with COVID-19: a prospective cohort
Bibliographic record
Abstract
Additional file 1: Figure S1: Number of patients admitted to ICU, patients with acute kidney injury, and patients who received kidney replacement therapy by month of admission. Figure S2: Proportions of patients with acute kidney injury and patients who received kidney replacement therapy by month of admission. Table S1: Baseline characteristics, laboratory biomarkers, treatment and outcomes by wave, AKI status and AKI staging. Table S2: Unadjusted associations between demographic characteristics and diagnosis of acute kidney injury for all patients and stratified by wave. Table S3: Indications for KRT between wave 1 and 2. Table S4: Adjusted associations between demographic characteristics and kidney replacement therapy for all patients and stratified by wave. Table S5: Comparison of daily cumulative fluid balance (%) by waves and sources of admission. Table S6: Unadjusted associations between COVID-19 treatments and AKI or KRT for all patients and stratified by wave. Table S7: Treatment and fluid balance for AKI or KRT patients only, stratified by day of diagnosis or KRT and wave of the pandemic. Table S8: Changes in serum creatinine and GFR values in alive patients from baseline, hospital discharge, and 90 days after hospital discharge Table S9: Associations between AKI, KRT and 24-hour cumulative fluid balance.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.470 | 0.031 |
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".