Association of <i>PLUNC</i> gene polymorphisms with susceptibility to nasopharyngeal carcinoma in a Chinese population
Bibliographic record
Abstract
Context: The Canadian Team to Improve Community-Based Cancer Care along the Continuum (CanIMPACT) is a group of researchers, primary care providers (PCPs), cancer specialists, patients and caregivers working to improve cancer care coordination between PCPs and cancer specialists. Previous research by CanIMPACT and others has identified problems related to communication, coordination, and continuity of care. Objective: Describe findings from qualitative interviews with cancer specialists on implementation of an online communication system with PCPs. Study Design: Hybrid type I effectiveness-implementation study that included a qualitative research component and a pragmatic RCT. Setting: Ottawa Hospital Cancer Program and primary care practices in the Champlain region. Population Studied: Cancer specialists (nurses, medical and radiation oncologists, program administrators). Interviews conducted with 12 cancer specialists. Intervention: Cancer-specific adaptation of Champlain BASE™ eConsult, an online communication system for PCPs and cancer specialists called “eOncoNote”. For patients receiving treatment for prostate or breast cancer, cancer specialists had an opportunity to participate in eOncoNote discussion with PCP for 4-6 months; for breast and colorectal cancer survivors, the eOncoNote discussion lasted for 1 year post discharge to the patient’s PCP. Results: Cancer specialists described limited PCP involvement in cancer care while patients received active treatment, with one-way communication and notes being “sent into a vacuum”. There was more communication with PCPs regarding patients with metastatic disease, comorbid conditions, after patients have completed treatment, or during palliative care. Patients and caregivers play a critical role in coordinating cancer care, helping to facilitate coordination. Lack of access to the same electronic medical record (EMR) among healthcare providers poses a barrier to cancer care coordination. eOncoNote had the potential to be useful tool but it was not used extensively. Conclusions: Accessing eOncoNote as a separate system was challenging to incorporate into the workflow, and cancer specialists highlighted the need for integration with their EMR. eOncoNote did not affect information sharing with PCPs, as there was limited uptake within primary care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".