Perioperative Characteristics and Outcomes of Patients Undergoing Anterior Cervical Fusion in July
Bibliographic record
Abstract
STUDY DESIGN: Retrospective national database analysis. OBJECTIVE: A national population-based database was analyzed to characterize the "July effect" on the perioperative outcomes of anterior cervical fusions (ACFs). SUMMARY OF BACKGROUND DATA: Perception biases exist regarding the outcomes of cervical spine surgery based upon the month of admission. METHODS: The Nationwide Inpatient Sample database was queried from 2009-2011. Patients who underwent an ACF in teaching and nonteaching hospitals were identified and separated into cohorts. Patients who were admitted in July were then compared with non-July admissions in both cohorts. Demographics, Charlson Comorbidity Index, length of stay, costs, postoperative complications, and mortality were assessed. RESULTS: A total of 52,499 ACF cases were identified in the Nationwide Inpatient Sample of which 26,831 (51.2%) were performed in teaching hospitals and 25,668 (48.8) in nonteaching institutions. July admissions represented 6.8% and 7.4% of cases in the teaching and nonteaching hospital cohorts, respectively. Among July admissions, the teaching cohort incurred a longer hospitalization than the nonteaching cohort (P < 0.05). In contrast, no significant differences in mortality or total hospital costs were demonstrated. In teaching institutions, the in-hospital complications associated with July patients included deep vein thrombosis and surgical site infection (P < 0.05), but this did not reach significance in nonteaching hospitals. Postoperative dysphagia and deep vein thromboses were also significantly more prevalent among July admissions in teaching hospitals compared with nonteaching institutions. CONCLUSION: This national study demonstrated that the early resident academic year was associated with a greater length of stay among July patients in teaching hospitals. This study did not demonstrate an increase in mortality or total hospital costs among July patients in either hospital cohort. In teaching hospitals, ACF-treated patients in July were associated with a greater incidence of postoperative thromboses and surgical site infection. In addition, the incidence of dysphagia was significantly greater among July patients in teaching hospitals than nonteaching 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".