The Comprehensive Geriatric Assessment Guide
Bibliographic record
Abstract
PURPOSE: Few opportunities exist for medical students and residents to receive feedback on specific geriatric skills because they are frequently unsupervised when assessing elderly patients. Patients and caregivers are currently an untapped source of clinical content feedback. The purpose of this study was to determine whether patients/caregivers could accurately complete a postassessment evaluation of trainees' clinical performance. METHOD: The authors developed the Comprehensive Geriatric Assessment Guide (CGAG) consisting of 36 yes/no/don't-remember questions that prompt the patient/caregiver to indicate what topics the trainee discussed during clinical assessment. In 2010, two raters independently listened to audio recordings of 10 trainee-administered clinical assessments, scoring them using the CGAG to determine interrater reliability. Next, 32 patients/caregivers completed a CGAG after a trainee-administered clinical assessment. Then, the authors compared the results with a "gold standard" CGAG of the encounter. RESULTS: Interrater reliability for the CGAG was high (90.4% agreement), indicating that the patients/caregivers were able to accurately complete the postassessment CGAG. Of 36 CGAG questions, 30 had patient/caregiver and gold standard agreement of over 80%; the remaining 6 had low agreement. CONCLUSIONS: Patients and caregivers were able to recall sufficient clinical assessment detail to potentially provide constructive feedback to medical trainees on their assessment skills via the CGAG. Six questions with low agreement will be reworded to improve clarity on future versions of the CGAG. Future investigations will help determine whether use of the CGAG during medical education may help trainees improve assessment performance and allow educators to track progress in geriatric competencies.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.075 | 0.081 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".