Algorithm of management of patients of older age groups with arthrosis of the knee joint with excess weight under martial law
Bibliographic record
Abstract
The provision of medical care to the civilian population under martial law has undergone significant changes due to the reorientation towards urgent care, the lack of polyclinic doctors and difficulties in accessing them, the mandatory receipt of an electronic referral from the family doctor, the destruction of hospitals, difficulties with logistics, the functional limitations of patients regarding the ability to move independently and the expectation of a long-term, sometimes several-day examination, the psychological state of patients, lack of funds. In the structure of the population of Ukraine, a relative increase in the percentage of sick older age groups was observed due to the emigration of the able-bodied population and children, the death of men and women, both military and civilian, mainly of working age, a decrease in the birth rate, which led to an increase in the burden of chronic pathology, and an increase in disability rates from the pathology of the musculoskeletal system. In addition, forced hypodynamia and the restriction of a balanced diet with an emphasis on eating food with a high carbohydrate content, the constant psychological pressure of the possibility of death, led to an increase in the number of overweight or obese people. This further complicated the course of chronic diseases in general and gonarthrosis in particular and limited the ability to move without assistance. All of the above led to difficulties in the process of establishing a final diagnosis, the appointment of treatment and subsequent medical examination was delayed or did not occur at all. Therefore, determining the tactics of managing patients of older age groups with excess body weight or obesity and gonarthrosis under martial law by creating an algorithm of actions for medical workers and patients is relevant. Keywords: degenerative-dystrophic diseases, overweight, body mass index, Leken index, Western Ontario & McMaster Universities Osteoarthritis index, protocol EuroQol-5D.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".