Introduction to the ASCO Quality Care Symposium
Bibliographic record
Abstract
The 2-day symposium was held November 30 and December 1, 2012, in San Diego, CA, and attracted over 660 attendees.In the pages of this issue are selected summations of the presentations from the invited speakers as well as distillations by the session chairs of the 15 abstracts selected for oral presentation.The 280 abstracts selected for poster presentations are available on the ASCO University Web site (http://meetinglibrary.asco.org/abstracts).The organizing committee selected abstracts for oral presentation on the basis of their perception of broad interest.Offerings included translational cancer health services research using large databases and population samples, as well as practical interventions that have improved quality in both large cancer care systems and small oncology practices.All of the selections were intended to promote the quality of cancer care for our patients and our colleagues, and were expected to stimulate additional quality improvement projects; research projects; and, above all, improve patient care.The idea for this meeting began during the year starting in June 2009, during which one of us (D.W.B.) served as ASCO President and another (C.C.E.) was Chair of ASCO's Quality Committee.The theme for that year was "Enhancing Quality through Innovation." 1 Three projects emerged from that year which were designed to improve the provision of quality care to patients with cancer.The first was recognizing and improving quality in real-life practice through the launch of the Quality Oncology Practice Initiative Certification Program.The second project, a rapid learning system to systematize our learning from every patient we treat, has now become the ambitious project CancerLinq. 2 In the third project, ASCO's Board of Directors charged the organizers with developing "a scientific assembly that fosters high quality research interactions and discussion among all investigators involved in quality and outcomes science research as recognized complement to the ASCO Annual Meeting."The result was this Quality Symposium, which aimed to bring together health services researchers (the basic scientists of quality improvement), quality improvement professionals (the translational researchers of quality improvement), and the practitioners of quality improvement.Early on, we recognized that inclusion of the informed patient is critical to any successful quality improvement effort.We sought and obtained the collaboration of the National Coali-
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 | 0.001 |
| 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".