Influence of selective comorbidity predictors on functional recovery after hip fracture in an older population
Bibliographic record
Abstract
AIM: The purpose of the study was to evaluate the influence of four comorbidities from the Cumulative Illness Rating Scale for Geriatrics (CIRS-G) and their severity on functional status outcome after a rehabilitation program measured by the Berg Balance Scale (BBS) in patients with hip fracture. METHODS: The study included 203 patients whose functional status was evaluated by the BBS at admission (Group 1), at discharge (Group 2) and 3 months after discharge (Group 3). Further comorbidity parameters from the CIRS-G were assessed: musculoskeletal impairment, neurological, vascular and cognitive impairment. For the evaluation of CIRS-G severity degree we used the range 0-4. RESULTS: At admission there were non-significant differences in mean values of BBS between parameters for the same CIRS-G severity degree. Significant differences between BBS values were noticed in the period after discharge (Group 2((musculoskeletal)); P<0.05, Group 2((neurological and cognitive)); P<0.01) and after 3 months of follow-up (Group 3((musculoskeletal, neurological and cognitive)); P<0.01). Higher effects of CIRS-G severity degree on BBS values in Group 2 and Group 3 for neurological impairment (η(2)(Group2)=29.76 and η(2)(Group3)=28.35) and even higher for cognitive impairment (η(2)(Group2)=34.35 and η(2)(Group3)=40.63) were noticed. CONCLUSION: Increase in CIRS-G severity degree of cognitive and neurological impairment in patients after hip fracture that were included in the rehabilitation program correlates closely with functional status after discharge and after 3 months of follow-up. Rehabilitation of patients after hip fracture should be mandatory for functional recovery regardless of the comorbidity and functional status.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".