Inferior vena cava collapsibility index and trauma severity in elderly fall injuries
Bibliographic record
Abstract
OBJECTIVE: Falls are a serious cause of morbidity and mortality among older people. One of the underlying causes of falls is dehydration. Therefore, ultrasonography has become an essential tool for evaluating volume status in the emergency department. However, the effect of volume status on falls in older people has not been evaluated before. The aim of this study was to determine the relationship between the inferior vena cava collapsibility index and the injury severity score in older patients who presented with fall-related injuries to the emergency department. METHODS: A total of 66 patients were included in the study. The injury severity score was used as the trauma severity score, and the Edmonton Frail Scale was used as the frailty scale. Volume status was evaluated with inferior vena cava collapsibility index. The primary outcome measure was defined as the correlation between inferior vena cava collapsibility index and injury severity score. Secondary outcome measures were defined as the effect of inferior vena cava collapsibility index and injury severity score on hospitalization and mortality. RESULTS: There was no significant correlation between injury severity score and inferior vena cava collapsibility index (p=0.342). Neither inferior vena cava collapsibility index nor injury severity score was an indicator of the mortality of these patients. However, injury severity score was an indicator of hospitalization. The mean Edmonton Frail Scale score was an indicator of mortality among older people who experienced falls (p=0.002). CONCLUSION: Inferior vena cava collapsibility index cannot be used to predict trauma severity in older patients who have experienced falls admitted to the emergency department.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".