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Record W4386736002 · doi:10.1097/jce.0000000000000605

Building a Clinical Engineering Department

2023· article· en· W4386736002 on OpenAlexaffabout
Christopher D. Gray, Andrew Ibey

Bibliographic record

VenueJournal of Clinical Engineering · 2023
Typearticle
Languageen
FieldHealth Professions
TopicQuality and Safety in Healthcare
Canadian institutionsChildren's Hospital of Eastern OntarioCarleton UniversityUniversity of Ottawa
Fundersnot available
KeywordsEngine departmentClinical engineeringEmergency departmentEngineeringLibrary scienceManagementEngineering managementMedicinePolitical scienceComputer scienceNursingHealth careLaw

Abstract

fetched live from OpenAlex

Corresponding author: Andrew A.M. Ibey, MEng, PEng, CCE, is the manager of clinical services in the Department of Biomedical Engineering, Children's Hospital of Eastern Ontario; Department of Systems and Computing Engineering, Carleton University; and Department of Mechanical Engineering, University of Ottawa, Ontario, Canada, and can be reached at [email protected]. Christopher D. Gray, BSc, MEng, at the time of submission of this article, was with the Department of Biomedical Engineering, University of Ottawa, Ontario, Canada, and can be reached at [email protected]. The authors declare no conflicts of interest.

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)0.007
metaresearch head score (Gemma)0.014
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: Methods · Consensus signal: none
Teacher disagreement score0.139
Threshold uncertainty score0.464

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.014
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0050.002
Scholarly communication0.0060.004
Open science0.0020.011
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.1390.058

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 head0.337
GPT teacher head0.610
Teacher spread0.274 · 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
GenreMethods

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".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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