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Record W4324129580 · doi:10.1007/s40279-023-01815-2

Effects of High-Intensity Interval Training Protocols on Blood Lactate Levels and Cognition in Healthy Adults: Systematic Review and Meta-Regression

2023· review· en· W4324129580 on OpenAlexafffund
Nithin Jacob, Isis So, Bhanu Sharma, Susan Marzolini, Maria Carmela Tartaglia, Paul Oh, Robin Green

Bibliographic record

VenueSports Medicine · 2023
Typereview
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsMcMaster UniversityToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsHigh-intensity interval trainingMedicineInterval trainingSports medicinePhysical therapyLactate thresholdInternal medicineBlood lactateHeart rateBlood pressure

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.004
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.015
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.013
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0150.025
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.000

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 head0.380
Teacher spread0.289 · 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 designMeta-analysis
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

Citations28
Published2023
Admission routes2
Has abstractno

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