Preliminary cross-sectional reliability and validity of the Skull Base Inventory (SBI) quality of life questionnaire
Bibliographic record
Abstract
BACKGROUND: The Skull Base Inventory (SBI) was developed to assess the quality of life of patients undergoing endoscopic or open approaches for anterior and central skull base pathologies. In this study, we sought to establish the discriminative and evaluative properties for this instrument. METHODS: The SBI was administered in a cross-sectional fashion to patients who previously had skull base surgery after treatment and then again 2 weeks after completing the instrument. Internal consistency, test-retest reliability, and construct validity were determined. Four constructs were evaluated with the following a priori hypotheses: lower scores will be seen in patients with 1.malignant versus benign histology, 2.a history of radiation versus none, and those with 3.recurrences versus no recurrence, and 4.items deemed relevant versus irrelevant by respondents. RESULTS: Fifty-two patients completed the questionnaire; 32 had endoscopic and 20 open surgeries. Internal consistency was good (>0.7 and <0.95) for all domains except one. Test-retest reliability was good (>0.70) for 38 of 41 items. Four constructs were evaluated and three were consistent with a priori hypotheses (p < 0.05). The instrument failed to confirm the hypothesis that malignant tumours are associated with poorer scores than benign. CONCLUSIONS: The SBI demonstrated preliminary reliability and validity for discriminative use.
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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 | 0.009 | 0.018 |
| 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 | 0.001 |
| 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 | 0.000 |
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".