8 * AN EVALUATION OF DELIRIUM MANAGEMENT IN THE ERA OF THE DaD TEAM
Bibliographic record
Abstract
Evidence-base: NICE identifies delirium as a major cause of morbidity and mortality amongst hospital patients, particularly when it is not diagnosed early and managed appropriately (Inouye et al., AJM 1999. 106(5) 563–573). A previous audit demonstrated that educational interventions improved trainee doctors' knowledge and recognition of delirium, but did not alter clinical practice when delirium had been identified (Chen et al., 2011). Change strategies: Several new activities have been started since the last audit cycle. All patients over 75 are now screened for cognitive impairment on admission. The Dementia and Delirium (DaD) team was set up to provide practical support to clinical teams; it consists of 2 geriatricians, a psychogeriatrician, 2 specialist dementia nurses and administrative support. The Delirium Bundle was developed to assist doctors and nurses with acute management of delirium. A series of educational videos (“Barbara's Story”) was produced and distributed widely to Trust staff. Change effects: A notes-review of all the DaD delirium referrals received in one month (69 in total) showed the majority were appropriate - recognising established delirium or patients at high risk of developing delirium. Two-thirds of cases were managed optimally, according to Trust guidelines. This compares favourably with findings from the previous audit cycle, in which only 2 of the 8 common precipitating factors were considered in 100% of the patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".