Hypernatremia in patients with severe traumatic brain injury: a systematic review
Bibliographic record
Abstract
BACKGROUND: Hypernatremia is common following traumatic brain injury (TBI) and occurs from a variety of mechanisms, including hyperosmotic fluids, limitation of free water, or diabetes insipidus. The purpose of this systematic review was to assess the relationship between hypernatremia and mortality in patients with TBI. METHODS: We searched the following databases up to November 2012: MEDLINE, EMBASE, and CENTRAL. Using a combination of MeSH and text terms, we developed search filters for the concepts of hypernatremia and TBI and included studies that met the following criteria: (1) compared hypernatremia to normonatremia, (2) adult patients with TBI, (3) presented adjusted outcomes for mortality or complications. RESULTS: Bibliographic and conference search yielded 1,152 citations and 11 abstracts, respectively. Sixty-five articles were selected for full-text review with 5 being included in our study. All were retrospective cohort studies totaling 5,594 (range 100-4,296) patients. There was marked between-study heterogeneity. The incidence of hypernatremia ranged between 16% and 40%. Use of hyperosmolar therapy was presented in three studies (range 14-85% of patients). Hypernatremia was associated with increased mortality across all four studies that presented this outcome. Only one study considered diabetes insipidus (DI) in their analysis where hypernatremia was associated with increased mortality in patients who did not receive DDAVP. CONCLUSIONS: Although hypernatremia was associated with increased mortality in the included studies, there was marked between-study heterogeneity. DI was a potential confounder in several studies. Considering these limitations, the clinical significance of hypernatremia in TBI is difficult to establish at this stage.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".