Влияние электромагнитного излучения терагерцового диапазона на частотах молекулярного спектра оксида азота на коагуляционный гемостаз у пациентов с различными формами стенокардии
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Aim. To study hypocoagulation effectiveness and mechanisms of electro-magnetic radiation, terahertz range, NO molecular specter frequencies (EMR THF-NO), in patients with various angina forms. Material and methods. The authors examined 80 patients with unstable angina, Class IIA and IIB (E. Braunwald classification), or effort angina, Functional Class II-IV (Canadian Cardiovascular Society). Twenty patients received standard medication therapy plus EMR THF-NO. Noteworthy, EMR THF-NO effects in unstable angina (UA) were studied in the absence of heparin therapy. The effects on main hemostatic parameters were studied: activated partial thromboplastin time (APTT), activated recalcification time (ART), prothrombin time, euglobulin fibrinolysis, fibrinogen (F) levels, antithrombin-III (At-III) activity, complex parameter of protein C system disturbances, Va factor resistance to activated C-protein. Results. EMR THF-NO demonstrated hypocoagulation effect in patients with stable angina (SA) and UA. In SA, hypocoagulation mechanism is explained by procoagulation potential reduction, by affecting the first (APTT and ART increase) and the third (F level decrease) coagulation phases. In US, it’s explained by increase in anticoagulant potential, due to At-III and modulation of initially disturbed fibrinolysis. Conclusion. EMR THF-NO should be included into complex therapy of angina patients, for greater hypocoagulation effect. EMR THF-NO could be used as an alternative method in patients with hypercoagulation and contraindications to special pharmaceutical hypocoagulation, or intolerance to these medications.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it