Bibliographic record
Abstract
Review Exercises Glossary Open Educational ResourceMedical terminology is commonly used in most medical settings, including veterinary clinics.This language is based on Greek and Latin terms and veterinary terminology is a subset of medical terminology.Just like learning a new language, it can be a daunting task.The primary objective of this OER is to introduce you to the medical terms in a simple and easy to understand format.This resource was designed as an introductory medical terminology course for the Veterinary Office Assistants at NorQuest College; however, it is likely to be useful for individuals in other veterinary medical settings as well.To assist with learning the complex language of veterinary medical terminology, each chapter has embedded H5P activities, including a final chapter review.We have also included content on abbreviations, common procedures and species-specific terms.We hope that this textbook will provide a thorough overview of not only medical terminology, but also introduce connections to other veterinary topics. Introduction | 1 How to Use This OERLearning medical terminology requires a lot of commitment.In order to get the full benefits of this textbook, daily review is important.Using the review exercises and activities in the chapters will help you learn and remember the content.In addition, we suggest creating flashcards with suffixes, prefixes, and combining forms to help you recall what you have learned.For instructors, our hope is that you can use the content and adapt it as necessary for your own programs and courses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.118 | 0.062 |
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".