Characterization of patients admitted to specialized geriatric acute care hospital units with the German version of the Standardized Evaluation and Intervention for Seniors at Risk (SEISAR) screening-instrument: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: The Standardized Evaluation and Intervention for Seniors at Risk (SEISAR) screening tool records major geriatric problems, originally applied in the emergency department. Particularly, the distinction of compensated and uncompensated problems is an interesting and new approach. Therefore, we translated the SEISAR in German language and used it to characterize patients in specialized geriatric hospital wards in Germany and to gather initial experience regarding its usability and practicability. METHODS: The tool was translated by three independent specialists in geriatric medicine and backtranslated for quality-assurance by a non-medical English native speaker. In a second step, 8 acute care geriatric hospital departments used the translated version to characterize all consecutive patients admitted over a period of one month between December 2019 and May 2020 at time of admission. RESULTS: Most of the 756 patients (78%) lived in an own apartment or house prior to hospital admission. Participants had on average 4 compensated and 6 uncompensated problems, a Barthel-Index of 40 pts. on admission with a median increase of 15 points during hospital stay, and a median length of stay of 16 days in the geriatric hospital department. CONCLUSION: SEISAR is an interesting standardized brief comprehensive geriatric assessment tool for the identification of compensated and uncompensated health problems in older persons. The data of this study highlights the number, variability, and complexity of geriatric problems in patients treated in specialized acute care geriatric hospital wards in Germany. TRIAL REGISTRATION: German Clinical trial register (DRKS-ID: DRKS00031354 on 27.02.2023).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".