Risk factors for unplanned readmissions in paediatric neurosurgery: a systematic review
Bibliographic record
Abstract
OBJECTIVES: Unplanned hospital readmission (UHR) after paediatric neurosurgery is an important indicator of surgical outcomes. As this field deals with complex cases, there is an increased likelihood of potential complications and the subsequent need for readmission. We estimated paediatric neurosurgery UHR rates globally and identified significant factors contributing to 30-day and 90-day UHR rates in children undergoing neurosurgical procedures. DESIGN: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. DATA SOURCES: Embase, Medline, CINAHL and Global Index Medicus databases were searched on 17th December 2023. ELIGIBILITY CRITERIA: We included studies that reported unplanned readmissions in the paediatric population within 30 days and 90 days of an index neurosurgical procedure. DATA EXTRACTION AND SYNTHESIS: Two independent qualified researchers screened studies and extracted data using standardised methods. Risk of bias assessment was done using the Newcastle-Ottawa scale. Narrative synthesis was performed. RESULTS: 2593 titles were identified following the search strategy. 52 studies were included after screening, full-text review and quality appraisal. Most studies were from the USA and are retrospective cohorts. The majority were cranial procedures (n=30), with common ones being shunt procedures for hydrocephalus and cranial tumour resections. 30-day readmissions ranged from 1.4% to 28% while 90-day readmissions ranged from 1.31% to 38.64%. 34 different risk factors were identified. Aetiology-related factors, procedure-specific complexities and age emerged as the three most common. Other common risk factors were complex chronic conditions, race, length of hospital stay and type of medical insurance. The patient's age was a significant non-modifiable predictor, with younger children generally facing higher odds compared with their older counterparts across different procedures. While early readmissions could be due to disease progression, some were linked to preventable causes. Heterogeneity was also present due to variations in definitions and inclusion of studies from both national databases and single institutions. CONCLUSIONS: Findings from this study contribute to a collective understanding of factors affecting unplanned readmissions in paediatric neurosurgery. UHRs reflect the interplay among surgical complexity, patient characteristics such as age and disease aetiology.
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 | 0.007 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.011 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".