National Thoracic Surgery Standards Implementation: Barriers, Enablers, and Opportunities
Bibliographic record
Abstract
BACKGROUND: Diagnosis and surgical treatment decision making for thoracic cancers is complex. Moreover, there is demonstrated variability in how each province in Canada delivers cancer care, resulting in disparities in patient outcomes. Recently, the Canadian Partnership Against Cancer (CPAC) published pan-Canadian evidence-based standards for the care of thoracic surgery cancer patients. This study was undertaken to assess the degree to which these standards were currently met in practice and to further understand the determinants to their implementation nationally. METHODS: This study was undertaken in two parts: (1) a national survey of thoracic surgeons to assess the perceived extent of implementation of these standards in their institution and province; and (2) formation of a focus group with a representative sample of thoracic surgeons across Canada in a qualitative study to understand the determinants of successful standards implementation. RESULTS: 37 surgeons (33% response rate) participated in the survey; 78% were from academic hospitals. The top categories of standards that were under-implemented included (a) quality assurance processes, data collection and clinician audit and feedback, and (b) ongoing regional planning and workload assessments for thoracic surgeons, and (c) pathology turnaround time target of two weeks and the use of a standardized synoptic pathology report format. Enablers, barriers, and opportunities for standards implementation contextualized the discussion within the focus group. CONCLUSION: Study results demonstrated variation in the implementation of surgery standards across Canada and identified the determinants to the delivery of high quality surgical care. Future work will need to include the promotion and development of quality improvement strategies and effective resource allocation that is aligned with the implementation of thoracic cancer surgery standards in order to improve patient outcomes.
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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 | 0.035 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".