Bicycle Set-Up Dimensions and Cycling Kinematics: A Consensus Statement Using Delphi Methodology
Bibliographic record
Abstract
Bicycle set-up dimensions and cycling kinematic data are important components of bicycle fitting and cyclist testing protocols. However, there are no guidelines on how bicycles should be measured and how kinematic data should be collected to increase the reliability of outcomes. This article proposes a consensus regarding bicycle set-up dimensions and recommendations for collecting cycling-related kinematic data. Four core members recruited panellists, prepared the document to review in each round for panellists, analysed the scores and comments of the expert panellists, reported the decisions and communicated with panellists. Fourteen experts with experience in research involving cycling kinematics and/or bicycle fitting agreed to participate as panellists. An initial list of 17 statements was proposed, rated using a five-point Likert scale and commented on by panellists in three rounds of anonymous surveys following a Delphi procedure. The consensus was agreed upon when more than 80% of the panellists scored the statement with values of 4 and 5 (moderately and strongly agree) with an interquartile range of less than or equal to 1. A consensus was achieved for eight statements addressing bicycle set-up dimensions (e.g. saddle height, saddle setback, etc.) and nine statements for cycling kinematic assessment (e.g. kinematic method, two-dimensional methodology, etc.). This consensus statement provides a list of recommendations about how bicycle set-up dimensions should be measured and the best practices for collecting cycling kinematic data. These recommendations should improve the transparency, reproducibility, standardisation and interpretation of bicycle measurements and cycling kinematic data for researchers, bicycle fitters and cycling related practitioners.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".