Delirium diagnostic 2012 to 2021, contribution of CAM and clinical nurses of the geriatric consultation team in CIUSSS de l’Estrie‐CHUS
Bibliographic record
Abstract
Abstract Background To evaluate the evolution of diagnostic of delirium following the introduction of CAM as a geriatric vital sign as part of The Specialized Approach to Senior Care (l’approche adaptée à la personne âgée, AAPA) in University Hospital CIUSSS de l’Estrie‐CHUS in 2014. Method Training on CAM and delirium was given in 2014. The nurses of the geriatric consultation team make recommendations/coaching to the treating teams during geriatric consultations. We analyze ICD‐10 data from > 65‐year‐olds hospitalized at the CHUS before (2012) and after (2016). A criteria base analysis of the files with diagnosis of delirium on the discharge letter or a geriatric consultation of November 2016 describes the considerations related to delirium. Result Of the 32,000 hospitalizations at CHUS in 2016, 13,000 are over 65 years old and 1296 have a geriatric consultation. 2.2% in 2012 to 2.7% in 2016 had delirium according to the exit letter. That is 3% of 65‐75 year olds and 8% of those over 75. 102 consecutive files of geriatric nursing consultations from November 2016 are reviewed. 63 new diagnoses of delirium with an average of 3 recommendations from the AAPA to the treatment team. Conclusion Delirium remains under‐documented at release and improves little from 2012 to 2021. The modalities of use of the current tools remain insensitive. The support of geriatric consultation teams remains necessary to support the development of AAPA against 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".