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Record W2401431370 · doi:10.3233/978-1-61499-022-2-97

Evaluation of Tensiometric Assessment as a Measure of Skill Degradation

2012· article· en· W2401431370 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueStudies in health technology and informatics · 2012
Typearticle
Languageen
FieldEngineering
TopicMechanics and Biomechanics Studies
Canadian institutionsSickKids FoundationHospital for Sick Children
Fundersnot available
KeywordsMeasure (data warehouse)Degradation (telecommunications)Computer scienceData miningTelecommunications

Abstract

fetched live from OpenAlex

This pilot study explored the use of tensiometry as a measure of retention of knot tying skills. Medical students learned a one-handed square knot tying technique. Their final performances were video recorded and these videos were uploaded on to a website. Students were divided into two groups: an observational learning group that had access to videos before a retention test, and a control group that did not. After a two-week retention period, all students came back and performed one more trial to test the amount of retention of the skill. Tensiometry was used to measure strengths of the knots before and after the retention period. The scores showed no significant difference between the groups (p>0.308) or tests (p>0.737). We interpret the results to suggest that tensiometry is not sensitive enough to detect degradation in the performance of fundamental clinical skills as they are forgotten after being taught.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score0.286

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.091
GPT teacher head0.383
Teacher spread0.292 · 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