Hospital‐associated deconditioning: Not only physical, but also cognitive
Bibliographic record
Abstract
INTRODUCTION: Hospital-associated deconditioning (HAD) or post-hospital syndrome is well recognized as reduced functional performance after an acute hospitalization. Recommendations for the management of HAD are still lacking, partly due to a poor understanding of the underlying processes. We aimed to review existing data on risk factors, pathophysiology, measurement tools, and potential interventions. MATERIALS AND METHODS: We conducted a systematic review from bibliographical databases in English, Spanish and French with keywords such as 'post-hospitalization syndrome' or 'deconditioning'. We selected studies that included people aged 60 years or older. Three researchers independently selected articles and assessed their quality. RESULTS: From 4421 articles initially retrieved, we included 94 studies. Most were related to risk factors, trajectories and measures, and focused on the physical aspects of deconditioning. Risk factors for HAD included age, nutritional status, mobility, and pre-admission functional status, but also cognitive impairment and depression. Regarding interventions, almost all studies were devoted to physical rehabilitation and environmental modifications. Only one study focused on cognitive stimulation. DISCUSSION: In the last decade, studies on HAD have mostly focused on the physical domain. However, neurological changes may also play a role in the pathophysiology of HAD. Beyond physical interventions, cognitive rehabilitation and neurological interventions should also be evaluated to improve deconditioning prevention and treatment in the hospital setting.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".