Two-year clinical follow-up enhances the diagnosis of early-stage hip osteoarthritis: data from check cohort
Bibliographic record
Abstract
OBJECTIVE: To provide a set of diagnostic criteria for early-stage hip osteoarthritis (OA) in primary care, using signs and symptoms monitored over 2 years in individuals with hip pain and/or stiffness. Additionally, the study aimed to see whether these factors were additive to factors based on baseline signs and symptoms only. METHODS: Data of the 543 persons with 735 symptomatic hips were collected from the prospective Cohort Hip and Cohort Knee cohort study. Using data from 5 to 10 years of follow-up, 24 experts (13 general practitioners, 11 secondary care physicians (6 rheumatologists and 5 orthopaedic surgeons)) inspected individuals' medical data on the presence of clinically relevant hip OA. Their diagnoses are used as reference standards. Backward selection method was used to provide models using the factors from baseline to 2 years of follow-up. Additionally, new models were combined with previously published models, using same selection method. Area under the curve (AUC) was calculated after each removal of factors in the final combined models. RESULTS: Radiographic factors and high-sensitive C reactive protein did not end up in any model with change factors only. AUC value (SD) of the final obtained model of change factors was 0.70 (0.01). Adding newly defined factors to previously published models significantly (p<0.0001) increased the AUC value to 0.75 (0.01). CONCLUSION: Final diagnostic criteria, consisting only of the factors obtained through history taking and physical examination, were able to detect early-stage hip OA associated with clinically relevant hip OA 5-10 years later, with 'moderate' precision.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".