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Record W4246482102 · doi:10.21203/rs.2.22301/v1

Recruitment patterns in a large international randomized controlled trial of perioperative care in cancer patients

2020· preprint· en· W4246482102 on OpenAlexaff
Aaron Gazendam, Anthony Bozzo, Patricia Schneider, Victoria Giglio, David Wilson, Michelle Ghert

Bibliographic record

VenueResearch Square · 2020
Typepreprint
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPerioperativeRandomized controlled trialMedicineCancerIntensive care medicineInternal medicineSurgery

Abstract

fetched live from OpenAlex

Abstract Introduction: The Prophylactic Antibiotic Regimens in Tumor Surgery (PARITY) randomized controlled trial (RCT) was the first study to prospectively enroll and randomize orthopaedic oncology patients in multiple centers internationally. The objective of this study was to describe recruitment patterns, examine the differences in enrollment across different PARITY sites, and to identify variables associated with differing levels of recruitment.Methods: Data from this study was obtained from the PARITY trial Methods Center and records of correspondence between the Methods Center and recruiting sites. We performed descriptive statistics to report the recruitment patterns over time. We compared recruitment, time to set up, and time to enroll the first patient between North American and international sites, private and public health care models, and the presence or absence of research personnel. Two-tailed non-paired t-tests were performed to test average monthly recruitment rates between groups. Results: A total of 602 patients from 36 North American and 12 international sites were recruited from 2013 to 2019. Average monthly enrollment increased each year of the study. North American sites were able to start up significantly faster than international sites (19.5 vs. 27.0 months p=0.04). However, international sites had a significantly higher recruitment rate once active (0.2 participants/month vs. 0.59 participants/month, p=0.023). Sites with research personnel were able to reach ‘enrolment ready’ status significantly faster than sites without research personnel (19.3 vs. 30.3 months, p=0.032), but there was no difference in recruitment once active (0.28 participants/month vs. 0.2 participants/month). Publicly funded sites were to recruit significantly more patients compared to private institutions (0.4/month vs. 0.17/month, p=0.03).Conclusions: As a collaborative group, the PARITY investigators increased the pace of recruitment throughout the trial, likely by increasing the number of active sites. The longer time to start-up at international sites may be due to the complex governing regulations of pharmaceutical trials. Nevertheless, international sites should be considered essential as they recruited significantly more patients per month once active. The absence of research support personnel may lead to delays in time to start up. The results of the current study will provide guidance for choosing which sites to recruit for participation in future collaborative clinical trials in orthopaedic oncology and other surgical specialties.

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)0.041
metaresearch head score (Gemma)0.071
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.959
Threshold uncertainty score0.216

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0410.071
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.087
GPT teacher head0.453
Teacher spread0.367 · 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.

Study designObservational
DomainMethods
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

Citations0
Published2020
Admission routes1
Has abstractyes

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