Effectiveness of combined drug containing calcium and vitamin D in treatment of primary dysmenorrhea in adolescent girls
Bibliographic record
Abstract
Objective.To increase the effectiveness of early diagnosis and to develop a differentiated approach to the treatment of adolescent girls with primary dysmenorrhea (PD). Material and methods.The study was conducted on 100 adolescent girls aged 15 to 17 years, diagnosed with primary dysmenorrhea, who were initially divided into study and control groups depending on the presence or absence of dysplasia of connective tissue (DCT). Then, to compare the two proposed treatment regimens, each group was divided by randomization into two subgroups. Basic therapy included drug NSAIDs and vitamin-mineral complex with a content of calcium 1 000 mg and vitamin D3800 IU per day. The second scheme: the basic treatment was added to the drug L-carnitine 800 mg, affecting collagen formation. Self-assessment of menstrual pain was performed using McGill’s pain questionnaire. The levels of free hydroxyproline, MMP-2 and -9, tumor necrosis factor α, serum magnesium, autoantibodies IgG to cardiolipin, antinuclear antibodies were determined. Results.In adolescent girls with DCT, the level of free blood hydroxyproline was almost two times higher than in patients with PD without DCT (276.78 ± 117.10 and 142.64 ± 90.40 ng/ml respectively to the groups; p < 0.05). In both groups, no cases of magnesium deficiency in the blood were detected. In the analysis of biochemical parameters in serum after treatment, in all groups all blood parameters significantly improved-free hydroxyproline decreased on average in 2.5 times, MMP in 3.0 times, TNF-α in 2.0 times, MMP-9 increased in 4.0 times and autoantibodies IgG to cardiolipin decreased on average in 4.0 times (p < 0.05). Conclusions.The proposed PD therapy with NSAIDs and vitamin-mineral complex, including calcium and vitamin D, the effectiveness of which is confirmed not only by clinical data, but also by biochemical studies, it is advisable to appoint patients with PD, including those with signs of DST. The results of our own research and literature data allow us to recommend prophylactic intake of vitamin and mineral complex containing calcium and vitamin D to adolescent girls suffering from PD, to improve the quality of life, reduce the drug load and prevent reproductive disorders in the future.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".