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Record W2907191906 · doi:10.1080/17461391.2018.1560506

Optimizing recovery to support multi‐evening cycling competition performance

2018· review· en· W2907191906 on OpenAlexaff
Normand A. Richard, Michael S. Koehle

Bibliographic record

VenueEuropean Journal of Sport Science · 2018
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle metabolism and nutrition
Canadian institutionsUniversity of British ColumbiaMinistry of Health
Fundersnot available
KeywordsEveningCyclingPhysical medicine and rehabilitationMedicinePhysical therapy

Abstract

fetched live from OpenAlex

Road criterium and track bicycle racing occur at high speeds, demand repeated high power outputs, last 10-90 min, and offer little chance for recovery after the event. Consecutive evenings of criterium and track racing are respectively known as speed-week or six-day events and take place in evening hours over the course of a week. Given the schedule and timing of these competitions, return to homeostasis can be compromised. No recommendations exist on how to optimize recovery for cyclists participating in these types of repeated evening competitions. Criterium and track cyclists spend considerable time, near and above the individual lactate threshold and therefore mostly utilize carbohydrate as their chief energy substrate. Henceforth, pre - and post-race nutrition and hydration is examined and recommendations are brought forward for carbohydrate, protein, and fluid intake. As evening high-intensity exercise perturbs sleep, strategies to optimize sleep are discussed and recommendations for an optimal sleep environment are given. Active recovery is examined, and the benefits of a short duration low intensity exercise reviewed. Passive recovery methods such as compression garments and cold water immersion are recommended, while evidence for massage, pneumatic compression devices, and neuromuscular electrical stimulation is still lacking. Optimizing recovery strategies will facilitate a return to the resting state following strenuous night competition.

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)0.004
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.994
Threshold uncertainty score0.790

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 science0.0010.000
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 head0.044
GPT teacher head0.308
Teacher spread0.264 · 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 designOther design
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

Citations15
Published2018
Admission routes1
Has abstractyes

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