Validation of prostate cancer index and SF-12 short forms.
Bibliographic record
Abstract
BACKGROUND: Assessment of prostate cancer (PCa) specific and generic health-related quality-of-life (HRQOL) is frequently omitted due to several obstacles, such as respondent burden and infrastructure-related limitations. We attempted to reduce the number of items of two commonly used HRQOL assessment tools, namely the UCLA PCa Index (PCI) and the RAND SF-12, with the intent of generating the most parsimonious, yet psychometrically valid and reliable HRQOL assessment tool. METHODS: The PCI and SF-12 were administered to 2415 radical prostatectomy patients, and re-tested in a convenience sample of 35 men with PCa. Multivariate linear regression models defined the most predictive and item-reduced SF-12 and PCI item combinations. These were subjected to standard psychometric reliability and validity tests. RESULTS: The 8-item PCI sexual function (SF) scale was reduced to three items. The 5-item PCI urinary function (UF) scale was reduced to three items. The 6-item SF-12 mental health scale was reduced to three items, and the 6-item SF-12 physical scale was also reduced to three items. The total number of items was reduced from 27 to 12 (44%). The item-reduced scales accounted for over 85% of full-scale variance. All reliability and validity tests yielded highly satisfactory results. CONCLUSION: We developed SF-12 and PCI short-forms, which consist of 12 of 27 (44%) original items and can be completed by most men within 2 minutes. The short-forms represent a valid substitute for the full scales, as they provide over 85% of full-scale information and demonstrate excellent reliability statistics. The short forms have the potential for decreasing respondent burden and infrastructure-related requirements, which may in turn promote HRQOL assessment after radical prostatectomy.
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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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".