Undeclared exposure to St. John's Wort in hospitalized patients
Bibliographic record
Abstract
AIM: The herbal medicine St. John's Wort (SJW) causes substantial decreases in the plasma concentrations of a range of co-administered drugs. Therefore, we evaluated the extent of systemic exposure to hyperforin and hypericin, two of the main constituents of SJW, in patients on admission and during hospital stay, and compared the results with known use of SJW as documented in the drug chart and detected in additional interviews. METHODS: One hundred and fifty patients aged > or = 18 years and admitted, between August 2000 and February 2002, to an internal medicine ward of a large German university hospital were included. Hyperforin and hypericin was determined in plasma by a sensitive liquid chromotography/mass spectometry (LC/MS/MS) method. To assess undeclared use of SJW the data were compared to information obtained from drug charts and from up to three interviews that had a particular focus on intake of herbal medicines and self-medication during hospitalization. RESULTS: Hyperforin was detected in 12 patients (plasma concentration on the first day of hospitalization = 12-100 ng ml(-1) in five patients and < 3 ng ml(-1) in seven), and hypericin in five patients (0.5-4.3 ng ml(-1)). Nine patients (6%) were taking/had taken SJW without the knowledge of the medical team and the pharmacist, who conducted the additional interviews, and 11 (7.3%) were taking/had taken SJW without the knowledge of the medical team alone. Seven of these patients were treated concurrently with drugs that can interact with SJW. CONCLUSIONS: Unrecognized use of SJW is frequent and may have an important influence on the effectiveness and safety of drug therapy during hospital stay.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".