Relationship between potential barriers to early mobilization in adult patients during intensive care stay using the Perme ICU Mobility score
Bibliographic record
Abstract
Background: Identifying barriers to early mobilization is essential for the management of patients in the intensive care unit (ICU).Our objective was to identify the potential barriers to early mobilization in adult patients using the Perme ICU Mobility Score (Perme Score) and its relationship with days of mechanical ventilation (MV) and length of stay in ICU.Methods: This was a pilot, observational, and prospective study.We included 142 adult patients admitted to a 14-bed ICU, in a fourth-level complexity hospital in Cali, Colombia.The Perme Score was used to evaluate potential barriers to mobility.We used the Spearman's correlation coefficient to find potential correlations between the number of barriers to mobility per patient and the duration of MV and ICU stay.Results: We identified significant inverse correlations between total days in MV and the total score of barriers to mobility at ICU admission (r = -0.773;p < 0.05) and at ICU discharge (r = -0.559;p < 0.05).Also, between ICU length of stay and total score of barriers to mobility at ICU admission (r = -0.420;p < 0.05) and at ICU discharge (r = -0.283;p < 0.05).Moreover, we found a significant correlation between total score of the barriers item and total Perme score (r = 0.91; p < 0.01).Conclusions: Using the Perme Score we identified potential barriers to mobility upon admission to the ICU that were maintained until discharge.Our findings indicate a strong positive correlation at ICU admission between the total Perme Score and the total score of "Category #2 -Potential Mobility Barriers" in the Perme Score.
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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".