Neurosurgical Economic and Readmission Trends After Extracranial Ventricular Shunts in the United States From 2009 to 2013
Bibliographic record
Abstract
Background: The aim of the study was to define the association between federal payer insurance and neurosurgical economic trends and readmissions after extracranial ventricular shunts (EVS) procedures and investigate these trends from 2009 to 2013 in the United States. Methods: We identified the procedure of insertion, replacement, or removal of EVS by applying the International Classification of Disease, Ninth Edition, Clinical Modification (ICD-9-CM) Procedure Codes of 231-235, 239, 242 and 243. Data were extracted for years 2009 to 2013. Year-wise distributions of index stays, readmission, percent readmission, cost for index stays and cost for readmissions for patients requiring EVS procedures who possess Medicare insurance (ME-patients) and Medicaid insurance (MD-patients) were described. Z-test statistic was used to compare the two groups. Results: During the 5 years of study, we recorded 149,220 index stays and 29,655 readmissions within 30 days involving the procedures of insertion, replacement, or removal of an EVS. Throughout the study period, hospital readmissions involving patients requiring procedures involving EVS consistently demonstrated both the highest annual mean cost for readmissions and the highest percentage of patient readmissions in regard to all neurosurgical procedures. The differences between the annual index stays and readmissions for ME-patients versus MD-patients requiring EVS were extremely statistically significant throughout the entire study period (P < 0.0001, P < 0.0001). The mean cost of readmissions within 30 days for all patients varied significantly from $19,005 to $23,499, with an average cost of $21,279 for readmissions occurring annually during the study period (P = 0.0161). The differences between the mean cost for index stays and readmissions for ME-patients versus MD-patients requiring EVS were extremely statistically significant throughout the entire study period (P < 0.0001, P < 0.0001). Conclusions: Federal payer insurance has a significant association with neurosurgical economic and patient readmission trends after EVS procedures in hospitals in the US. Further study is needed to investigate the etiology of these differences between patients’ payer insurance and their impact on clinical outcomes after EVS procedures. J Neurol Res. 2020;10(4):122-126 doi: https://doi.org/10.14740/jnr600
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".