Evaluation of disease burden, patient journey, unmet diagnosis and treatment needs of patients with HIP and knee osteoarthritis in Turkey: A study through Delphi Methodology
Bibliographic record
Abstract
Objective: To get information-driven insights from expert physicians regarding multiple aspects of the patient journey in knee and hip OA and establish a consensus for future studies and decision tree models in Turkey. Design: 157 questions were asked in total during this three-round modified Delphi-method panel to 10 physical medicine and rehabilitation specialists (2 have rheumatology and 3 have algology subspeciality), one orthopaedic surgeon and one algology specialist from anaesthesia specialty background. A consensus was achieved when 80% of the panel members agreed with an item. Contradictions between different disciplines were accepted as a non-consensus factor. Results: Panellists agreed that American College of Rheumatology classification criteria is mostly sufficient to provide an OA diagnosis in clinical practice, OA patients with ≥5 Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain or physical function score can be defined as moderate-to-severe OA if they have an additional ≥2 Kellgren-Lawrence (KL) score, a minimum improvement of 30% from baseline in WOMAC pain or function subscales or in PGA score can be accepted as moderate treatment response where ≥50% improvement from baseline in those scores as substantial response. Panellists stated that arthroplasty procedures need to be delayed as long as possible, but this delay should not jeopardize a beneficial and successful operation. Conclusions: These findings show that there is a significant disease burden, unmet treatment needs for patients with moderate-to-severe OA in Turkey from experts' perspective. Therefore, an updated systematic approach and decision tree models are needed to be implemented.
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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".