P.123 Functional status in neurosurgery and out of hospital outcomes: insights from a 12 year, 2300 patient retrospective cohort study
Bibliographic record
Abstract
Background: Limited information exists in neurosurgery regarding the association between functional status at hospital discharge and adverse events following discharge. Methods: A retrospective cohort study included all adults in one Boston teaching hospital who underwent neurosurgery between 2000-2012, survived hospitalization and had a Physical Therapist functional status assessment within 48-hours of discharge. 90-day post-discharge all-cause mortality was obtained from the US Social Security Administration Death Master File. Logistic regression analysis was used. Results: 2,369 patients were included, comprising 65% cranial and 35% spinal. Malignancy and trauma was 47% and 13%, respectively. 238 patients had independent functional status. 90-day mortality and readmission was 8.3% and 28%, respectively. Second, third and lowest quartile of functional status was associated with a 3.16 (95%CI 1.08-9.24), 6.00 (2.11-17.04) and 6.26 (2.16-18.16) respective increased odds of 90-day post-discharge mortality compared to patients with independent functional status, adjusting for age, gender, race, length of stay, presence of malignancy and Deyo-Charlson comorbidity. Good discrimination (AUC 0.82) and calibration (Hosmer-Lemeshow χ2 P = 0.23) were demonstrated. Adjusted odds of 90-day readmission in patients with the lowest quartile of functional status was 1.89 (1.28-2.80) higher than patients with independent functional status. Conclusions: Lower functional status at hospital discharge following neurosurgery is associated with increased post-discharge mortality and hospital readmission.
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 | 0.001 | 0.003 |
| 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) | 0.002 | 0.001 |
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".