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Record W4312846877 · doi:10.29173/oer35

The Language of Medical Terminology

2022· book· en· W4312846877 on OpenAlexfundno aff
Lisa Sturdy, Susanne Erickson

Bibliographic record

Venuenot available
Typebook
Languageen
FieldMedicine
TopicMedical and Biological Sciences
Canadian institutionsnot available
FundersNational Cancer InstituteCancer Research UKNational Institutes of HealthNational Heart, Lung, and Blood InstituteBritish Columbia Institute of TechnologyU.S. Navy
KeywordsTerminologyMedical terminologyComputer scienceLinguisticsNatural language processingPhilosophy

Abstract

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4.10 24-Hour Clock 4.11 Review Exercises Chapter V. Organization of the Body 5.1 Introduction to the Organization of the Body 5.2 Levels of Organization and Body Systems 5.3 Directional Terms and Body Planes 5.4 Body Cavities and the Abdominal Regions and Quadrants 5.5 Divisions of the Spine 5.6 Review Exercises Chapter VI.Body Systems 6.1 Introduction to Body Systems 6.2 Cardiovascular System 6.3 Respiratory System 6.4 Endocrine System 6.5 Female Reproductive System 6.6 Male Reproductive System 6.7 Lymphatic System 6.8 Urinary System 6.9 Nervous System 6.10 Integumentary System 6.11 Digestive System 6.12 Musculoskeletal System 6.13 Review Exercises Chapter VII.Medications 7.1 Introduction to Medications 7.2 Basics of Medications 7.3 Medication Routes and Forms 7.4 Intravenous (IV) Medications and Solutions 7.5 Medication Categories 7.6 Review Exercises Chapter VIII.Diagnostic Tests and Procedures 8.1 Introduction to Tests and Procedures 8.2 Laboratory Tests 8.3 Diagnostic Imaging: Radiology 8.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.028
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0020.005
Scholarly communication0.0080.008
Open science0.0020.003
Research integrity0.0020.008
Insufficient payload (model declined to judge)0.0280.029

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.

Opus teacher head0.035
GPT teacher head0.320
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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