Sociodemographic Factors and Quality of Life in Skull Base Surgery
Bibliographic record
Abstract
Objective: The primary objective was to examine the impact of sociodemographic factors on quality of life in patients undergoing skull base surgery. Design: Retrospective review of a prospective, multi-institutional, consecutive skull base cohort database Setting: Five international tertiary care centers including North America, Europe, and Australia. Participants: Patients treated surgically with benign or malignant neoplasms of the anterior, anterolateral, or central skull base. Main Outcome Measures: Sociodemographic, treatment, perioperative, and pathologic details were collected prospectively. Quality of life (QoL) measures including the skull base inventory (SBI), anterior skull base (ASB) questionnaire, and Sinonasal Outcome Test (SNOT-22) were administered to all patients at baseline, 2 weeks, 3, 6, and 12 months postoperatively. Patients were stratified based on sociodemographic factors determined a priori. Change in QoL from baseline was compared between groups over time using two-way repeat measure analysis of variance (ANOVA). Results: 178 patients were included in analysis. There were no statistically significant differences observed in change in QoL from baseline on SBI, ASB, or SNOT-22 when comparing sociodemographic factors ( p > 0.05 for all)—including age (less than vs. greater than mean), gender (male vs. female), income (less than vs. greater than median, lowest vs highest quartile), education (high school or less vs. post-secondary education), ethnicity (Caucasian vs. non-Caucasian). Subgroup analyses of patients treated with only endoscopic approaches or only pituitary pathology did not demonstrate any clinically significant differences in change in QoL from baseline. Conclusions: In surgically treated skull base patients, sociodemographic factors did not influence patients’ postoperative QoL. Publication History Article published online: 01 February 2023 © 2023. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".