Frailty and length of stay in older adults with blunt injury in a national multicentre prospective cohort study
Bibliographic record
Abstract
BACKGROUND: Patients suffering moderate or severe injury after low falls have higher readmission and long-term mortality rates compared to patients injured by high-velocity mechanisms such as motor vehicle accidents. We hypothesize that this is due to higher pre-injury frailty in low-fall patients, and present baseline patient and frailty demographics of a prospective cohort of moderate and severely injured older patients. Our second hypothesis was that frailty was associated with longer length of stay (LOS) at index admission. METHODS: This is a prospective, nation-wide, multi-center cohort study of Singaporean residents aged ≥55 years admitted for ≥48 hours after blunt injury with an injury severity score or new injury severity score ≥10, or an Organ Injury Scale ≥3, in public hospitals from 2016-2018. Demographics, mechanism of injury and frailty were recorded and analysed by Chi-square, or Kruskal-Wallis as appropriate. RESULTS: 218 participants met criteria and survived the index admission. Low fall patients had the highest proportion of frailty (44, 27.3%), followed by higher level fallers (3, 21.4%) and motor vehicle accidents (1, 2.3%) (p < .01). Injury severity, extreme age, and surgery were independently associated with longer LOS. Frail patients were paradoxically noted to have shorter LOS (p < .05). CONCLUSION: Patients sustaining moderate or severe injury after low falls are more likely to be frail compared to patients injured after higher-velocity mechanisms. However, this did not translate into longer adjusted LOS in hospital at index admission.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".