Bibliographic record
Abstract
Veterinary Clinical Pathology: An Introduction is a clinical pathology textbook primarily aimed towards veterinary students. The author, Marion Jackson, is a clinical pathologist at the Western College of Veterinary Medicine (WCVM) in Saskatchewan, Canada. The textbook follows the format of WCVM’s third-year clinical pathology course that is primarily case-based with mini lectures to support class assignments. Consistent with that pattern, this textbook has a thorough introduction and information section at the beginning of each chapter followed by numerous applicable cases that include both small and large animal examples. The chapters cover all the main topics of clinical pathology including hematology, with separate chapters on leukocytes and erythrocytes; biochemistry with separate chapters on the renal, hepatobiliary, muscular, and digestive systems as well as additional chapters devoted to lipids and proteins; fluids, electrolytes and acid-base balance; cytology; endocrinology and coagulation. I found this book an enjoyable read. The information is well-organized and flows nicely. It is not an exhaustive review so the reader is not distracted by minutia. At the end of each chapter’s information section is a list of “Nuggets,” which is a concise overview of important points for that topic. The examples are actual cases and therefore show the small changes and variety that accompany real-life scenarios. The glossary of terms is straightforward and nicely simplified. Some of the organizational features are less than optimal. There is duplication of photos with black and white photos accompanying the information within chapters and the identical color photos in a separate color section. Data tables for the cases are often overwhelming due to numerous columns as results and reference ranges are reported in both SI and conventional units. This feature is quite unique and does allow readers of both the USA and countries using international units such as Canada to use the cases, but it makes the data confusing. I highly recommend this textbook for clinical pathology students and professors. It is also appropriate for practicing veterinarians who wish to review clinical pathology but is less practical as a “quick go to reference” while working up a case. The index is clear and sends one to appropriate sections; however, because the book doesn’t go into every imaginable rule out, a practitioner who wants to be sure he/she has considered every single possibility may be disappointed. In addition, the chapters themselves do not include rule out lists, but this is compensated for by an appendix that contains short lists of rule outs for all indices. This feature may be handy for some individuals as the lists are all together and can be quickly reviewed; however, the lists are separate from background information.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.127 | 0.089 |
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".