Die Österreichische Apotheke in Zahlen: Jahrgang 2002
Bibliographic record
Abstract
Zeitversetzte Patienteninformation durch Apotheker ist therapiefördernd Subsequent supplementary information of the patient by the pharmacist supports therapy 5 Die Österreichische 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 während der FIP-Tagung in Vancouver 1997).Auf Österreich umgelegt wären 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 einjährige praktische Ausbildung in einer Apotheke mit abschließender Prüfung für 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 *) Personal ohne die 5 Apotheken, die auch eine öffentliche Apotheke betreiben **) Zahlen teilweise hochgerechnet *) exclusive of the five pharmacies which also operate a community pharmacy **) Figures partly projected Die Österreichische Apotheke in Zahlen Eine Sanierung des Gesundheitswesens über Einsparungen im Arzneimittelsektor ist schon deshalb illusorisch.A rehabilitation of the public health sector cannot be achieved simply by cutting down on drug expenses.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.051 | 0.016 |
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".