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Capillary supply of the tibialis anterior muscle in young, healthy, and moderately active men and women

2002· article· en· W2121850899 on OpenAlexaff
Michelle M. Porter, S. K. Stuart, M. Boij, Jan Lexell

Bibliographic record

VenueJournal of Applied Physiology · 2002
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsTibialis anterior muscleMedicineAnatomyInternal medicineSkeletal muscle

Abstract

fetched live from OpenAlex

Tibialis anterior muscle biopsies from moderately active men and women (21-30 yr; n = 30) were examined to determine potential gender differences in capillarization. The fiber type proportions [type I (T1) approximately 73%] were unaffected by gender. The men (M) had significantly (P < 0.001) larger fibers than the women (W), with a greater gender effect for type II (T2) fibers (P < 0.001). The M and W had similar capillary densities (CD approximately 390 capillaries/mm2), but the capillaries-to-fiber ratio (C/F) was higher in the M (M = 2.20 +/- 0.35, W = 1.66 +/- 0.32; P < 0.01). Capillary contacts (CC) were higher in T2 than T1 for the M (P < 0.01), but not W, and M had greater CC (P < 0.001). Both fiber area per capillary (FA/C) and fiber perimeter per capillary (FP/C) indicated that T1 fibers had greater capillarization than T2 fibers (P < 0.001). There were no gender differences in T1 FA/C and T2 FA/C or T1 FP/C, but a gender difference existed for T2 FP/C (M = 60.5 +/- 10.9, W = 70.6 +/- 13.4; P < 0.01). The gender difference for C/F could be explained by fiber size; however, the physiological implications of the difference in T2 FP/C remains to be determined. In conclusion, despite gender differences for fiber size, overall, capillarization was similar between the men and women.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.013
GPT teacher head0.241
Teacher spread0.228 · 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

Citations102
Published2002
Admission routes1
Has abstractyes

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