Development of a disease‐specific quality‐of‐life questionnaire for anterior and central skull base pathology—The skull base inventory
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Anterior and central skull base lesions and their surgical treatment (endoscopic or open approaches) can affect quality of life. A disease-specific instrument is needed to compare quality of life for different surgical approaches. STUDY DESIGN: Items were generated using a composite strategy consisting of chart review, systematic review of skull base instruments, expert interviews, and qualitative analysis of patient focus groups. A cross-sectional survey study was performed to reduce items based on an item impact score. METHODS: Charts of 138 patients who underwent skull base surgery were reviewed to identify physical items and domains. Five experts were interviewed for item and domain identification. Thirty-four patients were recruited into eight focus groups based on their surgical approach (open or endoscopic) and tumor location (anterior or central). Items were generated using a composite approach and then reduced into a final questionnaire using item impact scores. RESULTS: Chart review identified 47 physical items. Systematic review revealed nine relevant instruments with 217 relevant items. Experts identified 11 domains with 69 additional items. Qualitative analysis of focus groups generated 49 items. A total of 382 items were identified and reduced to 77 items after eliminating overlapping and irrelevant items. Further item reduction using item impact scores yielded 41 items. CONCLUSIONS: The Skull Base Inventory is a disease-specific quality-of-life instrument. Psychometric properties have yet to be tested. It may serve to compare quality of life for endoscopic or open procedures.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".