Additional file 1 of Metabolic disturbances potentially attributable to clogging during continuous renal replacement therapy
Bibliographic record
Abstract
Additional file 1. Table S1. Missing values. Table S2. Protocol CRRT - PFS. Table S3. Protocol CRRT - mFT. Table S4. Mixed effect model analyzing the effect of clogging on time-varying trajectories of sodium, bicarbonate and albumin-corrected calcium levels (correctedAlb Ca) as well as calcium substitution rate (CaSR.). Results present the impact of clogging on intercept (ΔI) and β-coefficient (Δβ) in comparison to non-clogging CRRT runs. *p<0.05, **p<0.01, ***p<0.001. Table S5. Estimates and 95%CI of the association between accumulative citrate exposure and clogging were calculated for the overall cohort (Model 1) and isolated mFT devices (Model 3). Model 2 displays the effect of PFS compared to mFT devices on citrate exposure. No significant differences in CRRT dosage were observed between filters with and without clogging. *p<0.05, **p<0.01, ***p<0.001. Table S6. Mixed effect model representing timed-dependent trajectories of renal biomarkers and factors with potential impact on clogging formation. Results present the impact of clogging on intercept (ΔI) and β-coefficient (Δβ) in comparison to non-clogging CRRT runs. Clogging was associated with increased plasma triglyceride levels. Analysis of first filters with clogging (first-clogging group, right columns) revealed a higher enteric nutritional intake in patients during the first onset of clogging. *p<0.05, **p<0.01, ***p<0.001. Figure S1. TMP.
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 | 0.002 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.790 | 0.079 |
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".