Use of the Pain Assessment Scales in Complex Examination of Women with Chronic Pelvic Pain
Bibliographic record
Abstract
The problem of improving the evaluation and treatment of women with chronic pelvic pain (CPP) syndrome is one of the biggest in modern gynecology. This is due to the high frequency of this pathology, numerous aspects of the pathogenesis, underlying psychological disorders and difficulties that arise in choosing an effective treatment. Treatment of patients with gynecological pathology is more effective in regard to assessment of pain intensity. The objective of our study was to establish the Pain Assessment Scales in complex examination of women with CPP. Objectives and methods: The main group consisted of 62 patients, who were diagnosed with the pelvic varicose veins (PVV). Control group consisted of 32 patients without pain and symptoms of gynecological pathology. The average age of women was 32.1 ± 2.4 years. Pain assessment was carried out according to Brief Pain Inventory, Numeric Pain Rating Scale, Short-form McGill Pain Questionnaire and Visual Analog Scale. Results: half of the patients have had sleep disorders, emotional lability with frequent changes of mood was observed in 79.0 % and a third of women were in depression. Most women felt cramping, aching and tender pain of moderate intensity (82.2 %). Previous therapy of chronic pain was transient in 79,0 % of cases and has resulted in relief of some patients (10 %). Conclusions: It is noted that a quantitative assessment of pain has no diagnostic value for the differential diagnosis of the causes of CPP and has prognostic value of routine assessment of pain during treatment.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".