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Record W2001457060 · doi:10.1515/cclm-2011-0857

Haematological and iron metabolism parameters in professional cyclists during the Giro d’Italia 3-weeks stage race

2012· article· en· W2001457060 on OpenAlexfundno aff
Roberto Corsetti, Giovanni Lombardi, Patrizia Lanteri, Alessandra Colombini, Rosella Graziani, Giuseppe Banfi

Bibliographic record

VenueClinical Chemistry and Laboratory Medicine (CCLM) · 2012
Typearticle
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsnot available
FundersWorld Anti-Doping Agency
KeywordsHaptoglobinAthletesFerritinMean corpuscular volumeRace (biology)PhysiologyMetabolismMedicineBiologyInternal medicineHematocritPhysical therapyBotany

Abstract

fetched live from OpenAlex

BACKGROUND: Haematological assessment is crucial for evaluating athletes healthy status. Professional athletes experience physiological modifications during competitions and over a season: the risk of sports anaemia is high. Few descriptions of haematological parameters behaviour during a 3-weeks cycling stage race have been published. METHODS: We studied nine professional cyclists engaged in the 2011 Giro d'Italia stage race. Pre-analytical and analytical phases tightly followed academic and anti-doping authorities' recommendations. Haematological and iron metabolism parameters were measured days -1 (pre-race), 12 and 22 during the race. RESULTS: Haemoglobin, red blood cells and haematocrit decreased during the race with a stabilisation in the second half, but final values were lower than baseline. Reticulocytes did not modify, whilst the immature reticulocyte fraction increased. No differences were found in red blood cells volume and corpuscular haemoglobin content, neither in iron metabolism markers. The acute phase proteins, haptoglobin and C-reactive protein, both increased over the race, while haemoglobin and haptoglobin were inversely related. CONCLUSIONS: These data are important for improving the knowledge of physiological modifications in haematological and iron metabolism parameters of professional athletes during highly demanding competitions. This is the first report, in the ambit of a stage race, in which the pre-analytical phase standardisation has been applied.

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)0.002
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 score0.018
Threshold uncertainty score0.786

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.350
Teacher spread0.320 · 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

Citations42
Published2012
Admission routes1
Has abstractyes

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