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Record W3090696497 · doi:10.3791/61598

Collection of Skeletal Muscle Biopsies from the Superior Compartment of Human Musculus Tibialis Anterior for Mechanical Evaluation

2020· article· en· W3090696497 on OpenAlexaff
Anthony L. Hessel, Daniel Hahn, Markus de Marées

Bibliographic record

VenueJournal of Visualized Experiments · 2020
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsCentre for Movement Disorders
Fundersnot available
KeywordsSkeletal muscleBiopsyMedicineMuscle biopsyMuscle fibreAnatomyPathology

Abstract

fetched live from OpenAlex

The mechanical properties of contracting skeletal fibers are crucial indicators of overall muscle health, function, and performance. Human skeletal muscle biopsies are often collected for these endeavors. However, relatively few technical descriptions of biopsy procedures, outside of the commonly used musculus vastus lateralis, are available. Although the biopsy techniques are often adjusted to accommodate the characteristics of each muscle under study, few technical reports share these changes to the greater community. Thus, muscle tissue from human participants is often wasted as the operator reinvents the wheel. Expanding the available material on biopsies from a variety of muscles can reduce the incident of failed biopsies. This technical report describes a variation of the modified Bergström technique on the musculus tibialis anterior that limits fiber damage and provides fiber lengths adequate for mechanical evaluation. The surgery is an outpatient procedure that can be completed in an hour. The recovery period for this procedure is immediate for light activity (i.e., walking), up to three days for the resumption of normal physical activity, and about one week for wound care. The extracted tissue can be used for mechanical force experiments and here we present representative activation data. This protocol is appropriate for most collection purposes, potentially adaptable to other skeletal muscles, and may be improved by modifications to the collection needle.

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 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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.601

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.078
GPT teacher head0.463
Teacher spread0.385 · 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 teacher head, 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

Citations4
Published2020
Admission routes1
Has abstractyes

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