Medical English Clear & Simple: A Practice-Based Approach to English for ESL Healthcare Professionals
Bibliographic record
Abstract
Introduction to American and Canadian Health Care and Cultural Concepts of Health and Wellness Concepts of Health and Wellness Professional Caring The Drugstore Calling the Doctor's Office The Musculoskeletal System Anatomy and Physiology Body Movement, Posture, Gait, Ambulation, and Position Treatment, Interventions, and Assistance The Cardiovascular System Anatomy and Physiology Assessing Function and Failure of the Cardiovascular/Circulatory System Treatment, Interventions, and Assistance The Respiratory System Anatomy and Physiology Common Disorders and Diseases of the Respiratory System Treatment, Intervention, and Assistance The Gastrointestinal System Anatomy and Physiology Common Complaints of the Gastrointestinal/Digestive System Treatments, Interventions, and Assistance: Food Safety and Stomach and Bowel Upset The Neurological System Anatomy and Physiology Common Complaints and Disorders of the Nervous System Treatments, Interventions, and Assistance Wounds and Viral and Bacterial Infections Pathophysiology Common Disorders and Diseases Treatments, Interventions, and Assistance Pharmacology and Medication Administration Pharmacology, Pharmacodynamics, and Pharmacokinetics Safety and Accuracy in Medication Administration Treatments, Interventions, and Assistance.
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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.042 | 0.022 |
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".