The 12 Ds of geriatric medical-psychiatry: A new format for geriatric case presentation
Bibliographic record
Abstract
Background : We present a new format for geriatric case presentation called the 12 Ds of Geriatric Medical-Psychiatry that facilitates an integrated discussion of both the physical and mental health issues that pertain to any geriatric patient. The format can be used to replace or to complement traditional medical model case presentation and can also be used as a teaching aid to provide the parameters for a holistic view of the geriatric patient. Methods : We developed the 12 Ds of Geriatric Medical-Psychiatry for case presentation by modifying the SBAR (situation, background, assessment, recommendations) with 12 clinical considerations that apply to any geriatric patient. Following implementation of the 12 Ds of Geriatric Medical-Psychiatry case presentation in our integrated team of geriatric medicine and psychiatry healthcare providers, we successfully used the 12 Ds model to present more than 180 patients and found the model easy to use and well received by learners and colleagues. Conclusion: The 12 Ds of Geriatric Medical-Psychiatry provides a comprehensive format to discuss the pertinent issues facing geriatric patients. When used in an SBAR format, it appears to be an efficient means for integrated case presentation and/or can be used as a tool for teaching and understanding a holistic view of complex geriatric cases.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.033 | 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".