Development, test reliability and validation of a classification for revision hip arthroplasty
Bibliographic record
Abstract
The objective of the study was to develop a valid and reliable classification system for failed hip arthroplasties. The study uses research principals derived from multi-attribute utility theory and consensus group techniques. The development of the severity measure was done in two phases. Phase I of the study included: (a) questionnaire development, (b) submission of the questionnaire to the respondents, (c) data synthesis of the responses and item reduction, and (d) classification development and inter-observer reliability testing. Phase II included: (a) resubmission of the instrument to the respondents for suggestions/feedback, (b) instrument revision by the co-investigators based on the respondents' second feedback, and (c) inter-observer reliability testing and intraoperative validity testing of the instrument. The questionnaires sought to capture expert opinion as to what clinical determinants obtained preoperatively (during patient interview, physical exam and review of plain radiographs - AP pelvis and hip lateral) that would in their clinical experience reveal intraoperative severity. There was an 80% (16/20) response rate from the outside experts invited to participate in the study. Based on item reduction and test retest analysis, a five-grade radiographic classification for the acetabulum as well as the femur was developed. This system was then reviewed by 13 of the initial outside experts (16, 80%) who participated in the first round. Inter-rater reliability testing of the final format of the classification revealed a weighted kappa statistic value of 0.88 between the two-blinded raters (inter-rater reliability) and 0.87 between the blinded raters and the reference standard (intraoperative validity). We conclude that the study developed a reliable and valid radiographic classification system for failed hip arthroplasty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".