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Record W2007444440 · doi:10.3109/03091902.2013.876111

The effect of probe placement on inter-trial variability when using the Cutometer MPA 580

2014· article· en· W2007444440 on OpenAlexaff
James P. Bonaparte, Jeffson Chung

Bibliographic record

VenueJournal of Medical Engineering & Technology · 2014
Typearticle
Languageen
FieldMedicine
TopicDiagnosis and Treatment of Venous Diseases
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsProtocol (science)MedicineClinical trialPathology

Abstract

fetched live from OpenAlex

There is limited data independently assessing the optimal use of the Cutometer MPA580. The purpose of this study is to test the hypothesis that the assessment of elastic recoil is significantly different when utilizing two different probe placement protocols. In protocol A, four trials were performed, in which the probe was removed from the skin between trials. In protocol B, the probe was not removed from the skin between trials. Fifty-four patients were enrolled and all completed the testing. When assessing elasticity (Ua/Uf), the inter-class correlation was 0.83 for protocol A and 0.48 for protocol B (p <0.001). There was no significant difference between individual trials for protocol A. Trial one of protocol B was significantly different (p < 0.001) than trials 2-4 for protocol B. Trial one of protocol B was not significantly different than any trial in protocol A. The results of this study suggest that the method in which a clinician performs repeated measure testing has a significant effect on the outcome measures when using the Cutometer. Removing the probe between trials appears to result in measures with higher reliability.

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)0.067
metaresearch head score (Gemma)0.220
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.353

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0670.220
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.280
Teacher spread0.272 · 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 designObservational
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

Citations13
Published2014
Admission routes1
Has abstractyes

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