The role of hydration in delirium at the end of life
Bibliographic record
Abstract
Delirium incidence in the advanced cancer population can be as high as 90% at the end of life. Hydration has been shown to be effective in improving delirium; however, its association with the course of delirium at the end of life is yet to be determined. The objective of this research was to estimate the extent to which hydration and related risk factors influence the course of delirium over time. Data were obtained from a database of 2515 persons admitted to palliative care centers in Quebec and Ontario. Persons who met pseudo clinical trial sampling criteria were included (n=1125). Group-based trajectory modeling and multivariate linear regression were used to identify subgroups of individuals with similar delirium trajectories during the first 30 days of admission and to determine what factors influence trajectory membership. A 6-group group trajectory model best fit the data. Hydration was not predictive of delirium group membership. Future work on hydration should focus on estimating its indirect effect on delirium.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".