A roadmap to advance delirium research: Recommendations from the NIDUS Scientific Think Tank
Bibliographic record
Abstract
Delirium is an acute disorder of attention and cognition. It occurs across the life span, yet it is particularly common among older adults, and is closely linked with underlying neurocognitive disorders. Evidence is mounting that intervening on delirium may represent an important opportunity for delaying the onset or progression of dementia. To accelerate the current understanding of delirium, the Network for Investigation of Delirium: Unifying Scientists (NIDUS) held a conference "Advancing Delirium Research: A Scientific Think Tank" in June 2019. This White Paper encompasses the major knowledge and research gaps identified at the conference: advancing delirium definition and measurement, understanding delirium pathophysiology, and prevention and treatment of delirium. A roadmap of research priorities is proposed to advance the field in a systematic, interdisciplinary, and coordinated fashion. A call is made for an international consortium and biobank targeted to delirium, as well as a public health campaign to advance the field.
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 | 0.208 | 0.273 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.014 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.025 | 0.038 |
| Open science | 0.016 | 0.025 |
| Research integrity | 0.059 | 0.070 |
| Insufficient payload (model declined to judge) | 0.029 | 0.019 |
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".