The Harris hip score: Do ceiling effects limit its usefulness in orthopedics?
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: The Harris hip score (HHS), a disease-specific health status scale that is frequently used to measure the outcome of total hip arthroplasty, has never been validated properly. A questionnaire is suitable only when all 5 psychometric properties are of sufficient quality. We questioned the usefulness of the HHS by investigating its content validity. METHODS: We performed a systematic review based on a literature search in PubMed, Embase, and the Cochrane Library for descriptive studies published in 2007. 54 studies (59 patient groups) met our criteria and were included in the data analysis. To determine the content validity, we calculated the ceiling effect (percentage) for each separate study and we pooled data to measure the weighted mean. A subanalysis of indications for THA was performed to differentiate the populations for which the HHS would be suitable and for which it would not. A ceiling effect of 15% or less was considered to be acceptable. RESULTS: Over half the studies (31/59) revealed unacceptable ceiling effects. Pooled data across the studies included (n = 6,667 patients) suggested ceiling effects of 20% (95%CI: 18-22). Ceiling effects were greater (32%, 95%CI:12-52) in those patients undergoing hip resurfacing arthroplasty. INTERPRETATION: Although the Harris hip score is widely used in arthroplasty research on outcomes, ceiling effects are common and these severely limit its validity in this field of research.
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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 | 0.294 | 0.435 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".