Disparities in neurosurgical care: Using length of stay to evaluate efficiency of care in New York City hospitals
Bibliographic record
Abstract
Objective: We sought to analyze public and private hospital patient cohorts in New York City (NYC) to assess differences in hospital access and outcomes from 2009-2022.Methods: Inpatient neurosurgical discharges, as determined by APR-DRG codes, from 2009-2022 were aggregated for seven NYC hospitals, four private and three public, via the Statewide Planning and Research Cooperative System (SPARCS). Statistical analyses (Z-tests) were performed in Python.Results: 325,351 patients were identified, 223,361 private and 101,990 public. Private hospitals had lower high-severity to low-severity and higher high-mortality to low-mortality risk ratios relative to public hospitals (p < .001). Public hospitals treated a higher proportion of stroke and trauma (p < .001). Average length of stay (LOS) was shorter at private hospitals compared to public (5.3 vs. 7.1 days, p < .001). Statistical significance remained when stratifying for illness severity and elective versus non-elective surgery status. Interestingly, cranial trauma cases were associated with a longer LOS in private hospitals relative to public (7.9 vs. 5.7 days, p < .001).Conclusions: While many factors influence outcomes in private versus public hospitals, LOS can mark the efficiency of care. LOS was shorter at private hospitals in all instances except with cranial trauma. Care efficiency is important for hospital reimbursement, which can directly impact available resources for patient care. These findings emphasize the need to further analyze patient accessibility to neurosurgical care at private hospitals and the resources necessary to support neurosurgical practices within public hospitals.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".