A systematic review of 30-day readmission after cranial neurosurgery
Bibliographic record
Abstract
OBJECTIVE The 30-day readmission rate has emerged as an important marker of the quality of in-hospital care in several fields of medicine. This review aims to summarize available research reporting readmission rates after cranial procedures and to establish an association with demographic, clinical, and system-related factors and clinical outcomes. METHODS The authors conducted a systematic review of several databases; a manual search of the Journal of Neurosurgery, Neurosurgery, Acta Neurochirurgica, Canadian Journal of Neurological Sciences; and the cited references of the selected articles. Quality review was performed using the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) criteria. Findings are reported according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. RESULTS A total of 1344 articles published between 1947 and 2015 were identified; 25 were considered potentially eligible, of which 12 met inclusion criteria. The 30-day readmission rates varied from 6.9% to 23.89%. Complications arising during or after neurosurgical procedures were a prime reason for readmission. Race, comorbidities, and longer hospital stay put patients at risk for readmission. CONCLUSIONS Although readmission may be an important indicator for good care for the subset of acutely declining patients, neurosurgery should aim to reduce 30-day readmission rates with improved quality of care through systemic changes in the care of neurosurgical patients that promote preventive measures.
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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.010 | 0.011 |
| 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) | 0.005 | 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".