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Record W2551643069 · doi:10.5604/20831862.1224098

Rapid weight loss in the context of Ramadan observance: recommendations for judokas

2016· review· en· W2551643069 on OpenAlexafffund
Asma Aloui, Hamdi Chtourou, Walid Briki, Montassar Tabben

Bibliographic record

VenueBiology of Sport · 2016
Typereview
Languageen
FieldMedicine
TopicDietary Effects on Health
Canadian institutionsUniversity of Toronto
FundersWorld Anti-Doping Agency
KeywordsWeight lossContext (archaeology)Perceived exertionMoodMedicineAthletesBody weightPhysical therapyWeight gainPsychologyPhysical medicine and rehabilitationObesityEndocrinologyHeart rateClinical psychologyBlood pressureBiology

Abstract

fetched live from OpenAlex

Judo is a weight-classified combat sport, and many athletes seek to compete at the lightest possible weight category to gain an advantage from competing against shorter/smaller, and supposedly weaker opponents. To achieve a desired weight, most judokas opt for rapid weight loss techniques. Short-duration maximal efforts are not greatly affected by "making weight", but prolonged and/or repeated exercise is significantly impaired. Negative effects on mood, ratings of perceived exertion, and cognitive function are also reported. Moreover, rapid weight loss reduces maximal cardiac output and glycogen stores, and impairs thermo-regulation. Limited empirical data suggest that Ramadan reduces judokas' performance, and this is likely to be exacerbated by attempts at rapid weight loss. Weight reduction during Ramadan tends to be counterproductive, and judokas who aim for a lower weight category are advised to attempt any desired reduction of body mass during the weeks leading up to Ramadan, rather than during the holy month.

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)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.114
GPT teacher head0.409
Teacher spread0.295 · 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

Citations16
Published2016
Admission routes2
Has abstractyes

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