Causes of 30-day readmission after neurosurgery of the spine
Bibliographic record
Abstract
OBJECT Thirty-day readmission has been cited as an important indicator of the quality of care in several fields of medicine. The aim of this systematic review was to examine rate of readmission and factors relevant to readmission after neurosurgery of the spine. METHODS The authors carried out a systematic review using several databases, searches of cited reference lists, and a manual search of the JNS Publishing Group journals (Journal of Neurosurgery; Journal of Neurosurgery: Spine; Journal of Neurosurgery: Pediatrics; and Neurosurgical Focus), Neurosurgery, Acta Neurochirurgica, and Canadian Journal of Neurological Sciences. A quality review was performed using STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) criteria and reported according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. RESULTS A systematic review of 1136 records published between 1947 and 2014 revealed 31 potentially eligible studies, and 5 studies met inclusion criteria for content and quality. Readmission rates varied from 2.54% to 14.7%. Sequelae that could be traced back to complications that arose during neurosurgery of the spine were a prime reason for readmission after discharge. Increasing age, poor physical status, and comorbid illnesses were also important risk factors for 30-day readmission. CONCLUSIONS Readmission rates have predictable factors that can be addressed. Strategies to reduce readmission that relate to patient-centered factors, complication avoidance during neurosurgery, standardization with system-wide protocols, and moving toward a culture of nonpunitive system-wide error and "near miss" investigations and quality improvement are discussed.
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 | 0.012 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".