Infections in hospitalised patients affected by end-stage diseases: a narrative overview
Bibliographic record
Abstract
AIM: To analyse the presence and treatment of infections in hospitalised terminal patients by identifying potential risk factors. METHODS: We conducted a retrospective study using health data from 229 terminally ill patients (evaluated by our hospital palliative care team (HPCT) hospitalised from January to December 2018. RESULTS: . The prevalence of infections was higher in patients with non-oncological diseases (n=47, 36.7%; p value 0.009). The potential risk factors identified for infections were the presence of: Parkinson's disease (n=15, 11.7%; p value 0.005), dysphagia (n=49, 38.3%; p value 0.007), bedding (n=15, 11.7%; p value 0.048), pressure ulcers (n=31, 24. 2%); p value 0.018), oxygen therapy (n=60, 46.9%; p value 0.050), urinary catheters (n=95, 74.2%; p value 0.038) and polypathology (2.3 vs 1.7; p value 0.022). Parkinson's disease (OR=5.973; 95% CI=1.292-27.608), dysphagia (OR=2.090; 95% CI=1.080-4.046) and polypathology (OR=1.220; 95% CI=1.015-1.466) were confirmed by a corrected logistic regression analysis. CONCLUSIONS: Infections and, consequently, antibiotic therapies, have a high prevalence in hospitalised patients with terminal disease. Potential risk factors for infections in these patients could be polypathology, dysphagia and Parkinson's disease. Patients with these conditions could benefit from prevention programmes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".