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Record W2323604306 · doi:10.1017/s1049023x14000776

An Analysis of Patient Presentations at a 2-Day Mass-participation Cycling Event: The Ride to Conquer Cancer Case Series, 2010-2012

2014· article· en· W2323604306 on OpenAlexaff
Adam Lund, Sheila A. Turris, Peter Wang, Justin Mui, Kerrie Lewis, Samuel J. Gutman

Bibliographic record

VenuePrehospital and Disaster Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicTravel-related health issues
Canadian institutionsFraser HealthIsland HealthUniversity of British Columbia
Fundersnot available
KeywordsWorkloadEvent (particle physics)Descriptive statisticsCyclingMedical emergencyPsychologyMedicineComputer scienceFamily medicineGeographyStatisticsMathematics

Abstract

fetched live from OpenAlex

OBJECTIVE: To analyze the unique factors involved in providing medical support for a long-distance, cross-border, cycling event, and to describe patient presentations and event characteristics for the British Columbia (BC) Ride to Conquer Cancer from 2010 through 2012. METHODS: This study was a 3-year, descriptive case series report. Medical encounters were documented, prospectively, from 2010-2012 using an online registry. Data for event-related variables also were reported. RESULTS: Providing medical support for participants during the 2-day ride was complicated by communication challenges, weather conditions, and cross-border issues. The total number of participants for the ride increased from 2,252 in 2010 to 2,879 in 2011, and 3,011 in 2012. Patient presentation rates (PPRs) of 125.66, 155.26, and 198.93 (per 1,000 participants) were documented from 2010 through 2012. Over the course of three years, and not included in the PPR, an additional 3,840 encounters for "self-treatment" were documented. CONCLUSIONS: The Ride to Conquer Cancer Series has shown that medical coverage at multi-day, cross-national cycling events must be planned carefully to face a unique set of circumstances, including legislative issues, long-distance communication capabilities, and highly mobile participants. This combination of factors leads to potentially higher PPRs than have been reported for noncycling events. This study also illuminates the additional workload "self-treatment" visits place on the medical team.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.019
GPT teacher head0.345
Teacher spread0.325 · 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

Citations15
Published2014
Admission routes1
Has abstractyes

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