Use of Risk Assessment Tool for Inpatient Traumatic Intracranial Hemorrhage after Falls in Acute Care Hospital Setting
Bibliographic record
Abstract
Severe injuries such as intracranial hemorrhage (ICH) are the most serious problem after falls in hospital, but they have not been considered in risk assessment scores for falls. We tried to determine the risk factors for ICH after falls in 20,320 inpatients (696,364 patient-days) aged from 40 to 90 years who were admitted to a tertiary-care university hospital. Possible risk factors including STRATIFY risk score for falls and FRAX™ risk score for fractures were analyzed by univariate and multivariate analyses. Fallers accounted for 3.2% of the patients, and 5.0% of the fallers suffered major injuries, including peripheral bone fracture (59.6%) and ICH (23.4%). In addition to STRATIFY, FRAX™ was significantly associated not only with bone fractures but also ICH. Concomitant use of risk score for falls and risk score for fractures might be useful for the prediction of major injuries such as ICH after falls.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".