VERTEBRAL PAIN SYNDROME AND QUALITY OF LIFE IN SENIOR WOMEN WITH VERTEBRAL FRACTURES DEPENDING ON THEIR QUANTITY AND LOCALIZATION
Bibliographic record
Abstract
Проанализированы особенности вертебрального болевого синдрома (ВБС) и качества жизни женщин в постменопаузе в зависимости от числа и локализации переломов тел позвонков. Установлено, что у лиц с двумя вертебральными переломами (ВП) и более уровень боли в грудном и поясничном отделах позвоночника, согласно индексу боли по опроснику МакГилла, был достоверно выше по сравнению с показателями у женщин без переломов на фоне отсутствия отличий по сравнению с лицами с одним ВП. Большинство показателей 11-составной цифровой рейтинговой шкалы при оценке ВБС на уровне грудного отдела позвоночника также были достоверно выше только в группе женщин с двумя ВП и более. Индекс боли в грудном отделе позвоночника был достоверно выше у женщин с переломами на этом же уровне, как и соответствующий параметр в поясничном отделе позвоночника у женщин с переломами на данном уровне по сравнению с показателями у контрольной группы. Не обнаружено достоверных отличий показателя качества жизни согласно анкете EuroQol-5D у женщин старших возрастных групп в зависимости от числа и локализации переломов тел позвонков, при этом отмечено ухудшение параметров жизнедеятельности согласно анкете Роланда-Морриса у пациенток с двумя ВП и более. The features of vertebral pain (VP) and quality of life in postmenopausal women were analyzed depending on the number and location of vertebral fractures (VF). It was found that the intensity of pain in thoracic and lumbar spines, according to McGill pain index, was significantly higher in patients with two or more VF compared to women without any fractures, and absence of differences in subjects with a single VF. Most indices of 11-component Numerical Rating Scale at the thoracic spine were signifi cantly higher only in females with two or more VF. In patients with thoracic spine fractures most parameters of VP measured at this level were significantly higher compared to control values, whereas in patients with lumbar spine fractures most indices did not differ from the corresponding parameters in subjects without fractures. There were no established significant differences of quality of life indices according to the EuroQol-5D questionnaire in senior women depending on the number and location of VF, while disturbances of daily activity parameters according to the Roland-Morris questionnaire were found in patients with 2 or more VF.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".