Abstract A11: Recruitment Monitoring Report of a Pan-Canadian Multi-center Study, the COOLS Trial.
Bibliographic record
Abstract
Abstract Screening logs are used for clinical trials to maintain the representativeness and validity of patient recruitment. It is especially critical in the active enrolment phase of a trial. The objectives of this study are: 1) To update the screening and recruitment status and 2) To discuss the challenges in patient recruitment and benefit in using screening log in a multi-centre trial. Methods: Using Microsoft Office Excel (2007), the Central Management Team (CMT) designed a screening log template with fields based on the 2010 Consolidated Standards of Reporting Trials (CONSORT). Data were collected with respect to patient information (Screening Date, Last Initial, First Initial, Referral Sources), lesion information (Anatomical Site, Diagnosis), recruitment (Recruitment Status - eligible, ineligible, refusal or not approached; Reasons to each unsuccessful recruitment status), Consent Date, and Baseline CT neck/chest (CT Date, CT Results). Built-in features such as customized dropdown lists with predetermined values were designed to maintain data consistency and text fields were used for detail explanations of scenario encountered. For patient confidentiality, the screening log was anonymous and without any patient identifiers. Screening logs were introduced, distributed, and explained to each Site Coordinator (SC) via Cisco WebEx teleconferencing tool, and were reviewed weekly by the CMT. Descriptive analysis was used for patient recruitment status. Results: From September 2010 to July 2012, six COOLS study sites (Vancouver, London, Toronto (Sunny Brook Hospital), Calgary, Halifax, Winnipeg) have been actively using the screening log. A total of 420 patients are screened with 167 (40%) were identified as ineligible according to the enrollment criteria, including anatomic site, the visibility of the margin of lesions, diagnosis, and tumor size. Among 253 eligible patients, 175 (69%) were consented, 59 (23%) refused to participate, and 19 (8%) are not approached. The main reasons for patient refusal were compliance for scheduled follow-up visits (29, 49%) and unwillingness to fill study forms (8, 14%). The main reasons for patients not been approached were history of not compliance from previous study participation (7, 37%) and scheduling conflict and failure in study referral (4, 21%). Eight consented patients failed to receive assigned treatment due to unavailability of the FV specialists. Issues are identified and communicated with PI and SC in a timely fashion for better solutions. Conclusions: The COOLS trial screening log is implemented to help monitor recruitment activity and quality of the subject screening. It is also a source to examine patient acceptance to the trial and new technology. The tool can be used as a communication channel to bridge a strong network between the CMT and SCs and to understand the strength and weakness of site referral and recruitment. Through early identification of recruitment issues, site-specific strategies can be developed and recruitment goal can be reached. (Supported by the Terry Fox Research Institute (2009-24) and the Canadian Cancer Society Research Institute (CCS-20336). Citation Format: Yi Ping Kelly Liu, Shane X. Duan, Alisa Kami, Sylvia F. Lam, Catherine F. Poh. Recruitment monitoring report of a pan-Canadian multicenter study, the COOLS Trial. [abstract]. In: Proceedings of the Eleventh Annual AACR International Conference on Frontiers in Cancer Prevention Research; 2012 Oct 16-19; Anaheim, CA. Philadelphia (PA): AACR; Cancer Prev Res 2012;5(11 Suppl):Abstract nr A11.
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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 | 0.013 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".