Socioeconomic Status and Days Alive and Out of Hospital after Major Elective Noncardiac Surgery
Bibliographic record
Abstract
BACKGROUND: Socioeconomic status is an important but understudied determinant of preoperative health status and postoperative outcomes. Previous work has focused on the impact of socioeconomic status on mortality, hospital stay, or complications. However, individuals with low socioeconomic status are also likely to have fewer supports to facilitate them remaining at home after hospital discharge. Thus, such patients may be less likely to return home over the short and intermediate term after major surgery. The newly validated outcome, days alive and out of hospital, may be highly suited to evaluating the impact of socioeconomic status on this postdischarge period. The study aimed to determine the association of socioeconomic status with short and intermediate term postoperative recovery as measured by days alive and out of hospital. METHODS: The authors evaluated data from 724,459 adult patients who had one of 13 elective major noncardiac surgical procedures between 2006 and 2017. Socioeconomic status was measured by median neighborhood household income (categorized into quintiles). Primary outcome was days alive and out of hospital at 30 days, while secondary outcomes included days alive and out of hospital at 90 and 180 days, and 30-day mortality. RESULTS: Compared to the highest income quintile, individuals in the lowest quintile had higher unadjusted risks of postoperative complications (6,049 of 121,099 [5%] vs. 6,216 of 160,495 [3.9%]) and 30-day mortality (731 of 121,099 [0.6%] vs. 701 of 160,495 [0.4%]) and longer mean postoperative length of stay (4.9 vs. 4.4 days). From lowest to highest income quintile, the mean adjusted days alive and out of hospital at 30 days after surgery varied between 24.5 to 24.9 days. CONCLUSIONS: Low socioeconomic status is associated with fewer days alive and out of hospital after surgery. Further research is needed to examine the underlying mechanisms and develop posthospital interventions to improve postoperative recovery in patients with fewer socioeconomic resources.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".