Higher requirements of dialysis in severe lithium intoxication
Bibliographic record
Abstract
Severe lithium poisoning is a frequent condition in the intoxicated intensive care unit population. Dialysis is the treatment of choice, but no clinical markers predicting higher requirement for dialysis have been identified to date. We analyze the characteristics of lithium overdose patients needing dialysis to improve lithium clearance, and identify the ones associated with higher dialysis requirement. This is an observational, retrospective study of 14 patients with lithium poisoning admitted from 2004 to 2009. Median age was 41.8 ± 16.1 years. Poisonings were acute in 7.1%, acute-on-chronic in 64.28%, and chronic in 28.5% of cases. Comparing clinical and biochemical data in patients requiring more than one dialysis session with those requiring only one session, the univariate analysis showed differences at admission in creatinine clearance (40.5 ± 23 vs. 73.3 ± 24.9 mL/min, P = 0.025), white blood cells (17,528 ± 3,530 vs. 11,580 ± 3360 cells/L, P = 0.007), and blood sodium concentration (134.8 ± 5.9 vs. 141.8 ± 8.4 mmol/L, P=0.035). We measured the degree of association between the number of sessions and the variables with partial correlations. High lithium levels (P = 0.006, r = 0.69), low creatinine clearance (P = 0.04, r = -0.55), and low blood sodium concentration (P = 0.024, r = -0.59) were associated with a greater number of dialysis sessions. The correlation remained significant for blood sodium concentration (P = 0.016, r = -0.67) after adjustment for creatinine clearance and initial lithium levels. Presence on admission of low creatinine clearance, low blood sodium concentration, and/or high lithium levels correlated with a higher number of dialysis sessions in severe lithium poisoning. These factors, especially low blood sodium concentration, are associated with higher dialysis requirements in severe lithium intoxication.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".