The harmful effects of plant preparations
Bibliographic record
Abstract
StreSzczenieMedycyna naturalna, chociaż stosowana jest od wieków, dopiero od niedawna zdobywa coraz większe zainteresowanie i traktowana jest jako remedium na wszelkie dolegliwości.Bardzo często nadużywana z powodu braku dostatecznej wiedzy na temat potencjalnych reakcji niepożądanych oraz możliwych interakcji między preparatami roślinnymi a poszczególnymi lekami.Ponadto medycyna tradycyjna stosowana przez dłuższy czas bez ograniczeń może prowadzić do trwałego uszczerbku na zdrowiu, a nawet śmierci.W pracy przedstawiono zagrożenia wynikające ze szkodliwości działania preparatów roślinnych podczas nieodpowiedniego stosowania oraz podczas łączenia ze sobą substancji współoddziałujących.Słowa kluczowe preparaty roślinne, suplementy pochodzenia naturalnego, interakcje leków, szkodliwe działanie.abStract Natural medicine, although it has been used for centuries, has only recently gained increasing interest and is treated as a remedy for all ailments.It is very often abused due to a lack of sufficient knowledge about potential adverse reactions and possible interactions between plant preparations and particular drugs.Moreover, traditional medicine used for a long period of time without restriction can lead to permanent damage to health and even death.This paper outlines the dangers of harmful effects of plant preparations when used inappropriately and when co-interacting substances are combined.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.006 |
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".