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Record W2905655317 · doi:10.7759/cureus.3764

Development, Evaluation, and Implementation of a New 3D Printed Tongue Depressor Dispenser

2018· article· en· W2905655317 on OpenAlexafffundabout
Chris Patey, Paul Norman, Nicole Bishop, Michael Bartellas, Adam Dubrowski

Bibliographic record

VenueCureus · 2018
Typearticle
Languageen
FieldHealth Professions
TopicHealth Policy Implementation Science
Canadian institutionsMemorial University of Newfoundland
FundersAtlantic Canada Opportunities Agency
KeywordsMedicine3D printingSAFER3d printedOperations managementTongue3d printerComputer scienceBiomedical engineeringMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

The purpose of this technical report was two-fold. First was to describe the concept, development and initial implementation of a three-dimensional (3D) printing network focussed on manufacturing simulators and simple devices necessary to the functioning of rural hospital and clinics. Second was to describe the design, fabrication and user-based evaluation of a cost-effective tongue depressor dispenser. The initial successful setup and implementation of the 3D printing network were modelled using four of the five implementation constructs derived from the Consolidated Framework for Implementation Research (CFIR). The 3D printing of the tongue depressor dispenser was found to be an effective and economic initiative. Without considering the initial design costs, the materials costs were estimated at $6 Canadian per dispenser. After installation of the dispenser in a busy emergency department, hospital leadership and staff viewed it as a safer option to the current dispense, and more affordable.

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)0.017
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.540
GPT teacher head0.685
Teacher spread0.146 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations2
Published2018
Admission routes3
Has abstractyes

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