Comparing short form and RAND physical and mental health summary scores: Results from total hip arthroplasty and high-risk primary-care patients
Bibliographic record
Abstract
OBJECTIVES: Summary physical health scores for the Short Form (SF) measures are computing using positive weights for physical items and negative weights for mental health items. Mental health summary scores use positive weights for mental items and negative weights for physical. The RAND Health Status Inventory (HSI) measures do not use negative weights. Do these different approaches to scoring matter? The objective was to compare summary scores using both the SF and RAND-HSI. METHODS: SF-36 and the Health Utilities Index Mark 3 (HUI3) were administered to a cohort of patients waiting for elective total hip arthroplasty (THA). SF-12 and HUI3 were administered to a cohort of high-risk primary-care patients. Summary scores were generated and compared. Single-attribute utility scores for emotion in HUI3 were also computed. Canadian and US norms for SF, RAND-HSI, and HUI3 were used to interpret results. RESULTS: For THA patients, mean physical health scores were 28 and 36 for SF and RAND-HSI. Mean mental health scores were 55 and 42. For the primary-care patients, the scores were 34 and 36 for physical and 46 and 40 for mental health. CONCLUSIONS: SF and RAND-HSI provided somewhat similar summary scores in the THA study. However, SF and RAND-HSI mental health scores differed in the primary-care patient cohort and results from HUI3 corroborate the mental health deficits identified by the RAND-HSI. It may be wise for investigators to use both SF and RAND-HSI scoring systems.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".