Bibliographic record
Abstract
($60 for APhA members).Diagnostic Imaging for Pharmacists is a combined textbook and partial pharmacopeia that provides explanations of both diagnostic and therapeutic radiographic modalities and discussions of applicable contrast media, adjuncts, and therapeutic agents.It offers basic descriptions of the principles of imaging used in various techniques and delves more specifically into the associated pharmaceuticals necessary for imaging and therapy.This text is, according to the website of the American Pharmacists Association, "the only diagnostic imaging reference written specifically for pharmacists."With its focus predominantly on the pharmaceuticals used within the radiology department, the most likely target audience would be pharmacists with responsibilities for the provision of care in this setting, an evolving role that the authors of this text advocate.Other pharmacists with an interest in the topic and those who regularly manage care for patients undergoing radiological procedures may also find this text useful.The lead editors for this text are both pharmacists with interests in nuclear medicine.Smith holds a BScPharm and a PhD and is past chair of the University of Saint Joseph School of Pharmacy in West Hartford, Connecticut.Weatherman holds the credentials PharmD, BCNP, and FAPhA.She is a clinical associate professor of pharmacy practice at Purdue University in Indianapolis, Indiana, with primary responsibilities in nuclear pharmacy and diagnostic imaging.The 10 contributing authors are a group of physicians, pharmacists, radiation safety officers, and technical experts from various academic, industry, and medical institutions across the United States.This collection of authors has the broad scope and knowledge base necessary to produce a text such as this.
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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.011 |
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".