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Record W1984871092 · doi:10.1042/cs20080632

Interval exercise is a path to good health, but how much, how often and for whom?

2008· review· en· W1984871092 on OpenAlexaff
Maureen J. MacDonald, Katharine D. Currie

Bibliographic record

VenueClinical Science · 2008
Typereview
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInterval trainingPhysical therapyAthletesCardiovascular fitnessOverweightMedicineHigh-intensity interval trainingDiseaseAerobic exerciseElite athletesPhysical fitnessPhysical medicine and rehabilitationObesityInternal medicine

Abstract

fetched live from OpenAlex

Interval exercise training has now been examined in a wide variety of individuals, ranging from elite athletes to patients with severe cardiovascular disease. The advantages of interval exercise training programmes in comparison with constant intensity exercise programmes are that they appear to deliver superior improvements in several cardiovascular risk factors, fitness and performance. Depending on the design, some interval exercise programmes result in a range of benefits, even though the time commitment may be dramatically less than more traditional continuous intensity programmes. In the present issue of Clinical Science, a study by Tjønna and co-workers demonstrates that aerobic interval training may also be a powerful tool in combating the increased cardiovascular risk observed in overweight adolescents.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.162
GPT teacher head0.460
Teacher spread0.297 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations23
Published2008
Admission routes1
Has abstractyes

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