Die Österreichische Apotheke in Zahlen: Jahrgang 2002
Bibliographic record
Abstract
Zeitversetzte Patienteninformation durch Apotheker ist therapiefrdernd Subsequent supplementary information of the patient by the pharmacist supports therapy 5 Die sterreichische Apotheke in Zahlen Kapitel 1 Falsch eingesetzte Medikamente verursachen in den USA Milliarden an Kosten: "84 billions US $ are spent for drug-related morbidity and mortality in the USA" (Zitat.Prof.L.M.Strand, Universitiy of Minnesota whrend der FIP-Tagung in Vancouver 1997).Auf sterreich umgelegt wren das 1,98 Mrd.. "84 billions US $ are spent for drug-related morbidity and mortality in the USA" (Prof.L.M.Strand, Universitiy of Minnesota during the FIP-Congress in Vancouver in 1997).Transferred to Austria this would mean 1,98 billions.*) inkl.196 Aspiranten nach erfolgreichem Abschluss des Pharmaziestudiums ist eine einjhrige praktische Ausbildung in einer Apotheke mit abschlieender Prfung fr den Apothekerberuf erforderlich.*) including 196 "Aspiranten" (= trainees) after having completed the university studies of pharmacy, graduates have to do one year of practical training in a pharmacy followed by a final examination in order to be allowed to work as pharmacists.**) Zahlen teilweise hochgerechnet **) Figures partly projected Die sterreichische Apotheke in Zahlen *) Personal ohne die 5 Apotheken, die auch eine ffentliche Apotheke betreiben **) Zahlen teilweise hochgerechnet *) exclusive of the five pharmacies which also operate a community pharmacy
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.018 |
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; both teacher heads agree on what is shown here.
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".