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Record W2072086296 · doi:10.1089/lrb.2006.4.159

Lymphoscintigraphy to Evaluate the Effect of High versus Low Intensity Upper Body Dynamic Exercise on Lymphatic Function in Healthy Females

2006· article· en· W2072086296 on OpenAlexaff
Kirstin N. Lane, Lianne B. Dolan, Dan Worsley, D. L. McKenzie

Bibliographic record

VenueLymphatic Research and Biology · 2006
Typearticle
Languageen
FieldMedicine
TopicLymphatic System and Diseases
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsLymphatic systemMedicineIntensity (physics)Physical medicine and rehabilitationPhysical therapyPathologyPhysics

Abstract

fetched live from OpenAlex

BACKGROUND: Little is known about the acute effects of exercise on the lymphatic system. Thus, the purpose was to determine the effect of low versus high intensity upper body exercise on lymphatic function in healthy females. METHODS AND RESULTS: On separate days, eight females performed either HI: 12 repeated sets of arm cranking for 2.5 min at 0.6 W.kg-1, followed by 2.5 min of rest; or LO: 12 repeated sets of arm cranking for 2.5 min at 0.3 W.kg-1, followed by 2.5 min of rest. One min spot views were taken with a gamma-radiation camera immediately after injection of 99mTc-antimony colloid and every 10 min thereafter to measure the clearance rate (CR) from the first and fourth finger-web of each hand. Radiopharmaceutical uptake in the axillary regions (AX) at 65 min postinjection was also measured. Clearance from the injection sites was linear and expressed as a slope (% administered activity.min-1). HI resulted in significantly greater CR (-0.24%.min-1+/-0.06) than LO (-0.19%.min-1+/-0.05; p=0.003). A similar trend was seen in AX (HI: 6.3%+/-1.6, LO: 4.8%+/-1.1, p=0.004). CONCLUSIONS: The results indicate that an arm cranking protocol of higher intensity is more effective in promoting lymphatic clearance from the hand. Further, the high intensity protocol may be useful in the functional evaluation of the lymphatic system in breast cancer survivors and warrants further investigation.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.093
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.042
GPT teacher head0.378
Teacher spread0.336 · 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 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

Citations11
Published2006
Admission routes1
Has abstractyes

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