The validity of clinical examination in the diagnosis of loosening of components in total hip arthroplasty
Bibliographic record
Abstract
We analysed follow-up data from 18,486 primary total hip arthroplasties performed between 1967 and 2001 to assess the validity of clinical procedures in diagnosing loosening of prosthetic components. Sensitivity, specificity and predictive values were estimated with the radiological definition of loose or not loose as the 'gold standard'. The prevalence of acetabular loosening increased from 0.6% to 13.9% during the period of the study and that of femoral loosening from 0.9% to 12.1%. Sensitivities and positive predictive values were low, suggesting that clinical procedures could not replace radiological assessment in the identification of loose prostheses. Specificities and negative predictive values were constantly above 0.86. The possibility of there being a prosthesis which is not loose in asymptomatic patients was consequently very high, particularly during the first five to six years after operation. The necessity of periodic clinical and radiological follow-up examinations of asymptomatic patients during the first five to six years after operation remains questionable. Symptomatic patients, however, require radiological assessment.
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 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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".