Risk factors on length of stay among pulmonary tuberculosis patients: A systematic review and meta-analysis
Bibliographic record
Abstract
Pulmonary Tuberculosis (PTB) remains a pressing public health concern. Long hospital stays for PTB patients can overburden both patients and healthcare systems. To identify the key factors contributing to extended length of stay (LOS) in PTB patients. Four electronic databases (PubMed, Scopus, Embase, and CINAHL) were systematically searched from inception to January 1, 2023. The articles were screened and performed according to Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA). Inclusion criteria were PTB patients diagnosed by doctors and studies reporting factors affecting LOS. Exclusion criteria were review articles, case study, conferences abstract, and proceedings. Study quality was assessed using the Newcastle-Ottawa Scale (NOS). A random-effects model was used to analyzed risk factors for LOS. Heterogeneity was employed using I 2 and Q statistics. Forest plots displayed effect sizes (ES) and 95% confidence intervals. STATA 14.2 was used for meta-analysis. A total of 1,190 studies were screened from reputable electronic databases, six studies comprised of 9,231 participants were included. Meta-analysis revealed that they are six risk factors associated with longer LOS including; older age (OR 1.50, 95% CI 1.07–2.09, p=0.019), comorbidity (OR 1.44, 95% CI 1.17–1.78, p=0.001), HIV patient (OR 1.40, 95% CI 1.16–1.69, p=0.001), patients with ADR (OR 2.19, 95% CI 1.47–3.26, p<0.001), MDR TB (OR 3.16, 95% CI 2.31–4.32, p<0.001), and miliary TB (OR 1.37, 95% CI 1.10–1.70, p=0.004) with minimal heterogeneity [(I 2 =34.2%, p=0.207), (I 2 =43.1%, p=0.118), (I 2 =0.0%, p=0.573), (I 2 =0.0%, p=0.723), (I 2 =0.0%, p=0.366), and (I 2 =0.0%, p=0.753), respectively]. There was no evidence of publication bias according to Begg's and Egger's test. In conclusion, six risk factors were identified as significantly associated with longer hospital stays in PTB patients: older age, comorbidities, HIV infection, ADR, MDR-TB, and miliary TB. These findings highlight the importance of targeted interventions for these high-risk groups to reduce LOS and alleviate the burden on healthcare systems. The results are based on a meta-analysis of six studies with minimal heterogeneity, and no evidence of publication bias was found. Future research should focus on exploring additional factors influencing LOS, particularly in diverse populations, and evaluating the effectiveness of interventions to shorten hospital stays. Additionally, studies examining the impact of healthcare infrastructure and resource allocation on LOS could provide valuable insights for improving patient outcomes. This study was registered with PROSPERO, CRD4203390615
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.047 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".