Les plantes diurétiques à l’officine
Bibliographic record
Abstract
Recently, patients have been looking for complementary and natural therapies, particularly herbal medicine. Faced with self-medication and the multitude of advices found on the Internet, the pharmacist is an important consulting partner when delivering plants. In this thesis, nineteen medicinal plants are listed in the French Pharmacopoeia (XIth Edition): birch (Betula pendula L.), borage (Borrago officinalis L.), heather (Erica cinerea L.), buchu (Barinus betulina Thunb.), restharrow (Oninis spinosa L.), quack grass (Elytrigia repens L.), juniper (Juniperus communis L.), sour cherry (Prunus cerasus L.), orthosiphon (Orthosiphon stamineus Benth.), nettle (Urtica dioïca L.), mouse-ear hawkweed (Hieracum pilosella L.), dandelion (Taraxacum officinale Weber), horsetail (Equisetum arvense L.), elderberry (Sambucus nigra L.), linden (Tilia sp.), goldenrod (Solidago virgaurea L.), and the Canada fleabane (Erigeron canadensis L.). These plants have diuretic properties and are used in the case of water retention, slimming regimen or urinary lithiasis. For an easy use in the pharmacy, they are presented on an indication board. Following this infatuation, the chemist is often the first person consulted by patients to take medicinal advices. He must propose the most adapted plant(s), ensuring there is no contra-indication to use them, to verify the lack of drug interactions, to be able to know the limits of his advices and to recall the rules for a good use.
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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.039 | 0.019 |
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".