Multiple factors influence compliance with colorectal cancer staging recommendations: an exploratory study
Bibliographic record
Abstract
BACKGROUND: For patients with colorectal cancer (CRC) retrieval by surgeons, and assessment by pathologists of at least 12 lymph nodes (LNs) predicts the need for adjuvant treatment and improved survival. Different interventions (educational presentation, engaging clinical opinion leaders, performance data sent to hospital executives) to improve compliance with this practice had variable results. This exploratory study examined factors hypothesized to have influenced the outcome of those interventions. METHODS: Semi-structured interviews were conducted with 26 surgeons and pathologists at eleven hospitals. Clinicians were identified by intervention organizers, public licensing body database, and referral from interviewees. An interview guide incorporating open-ended questions was pilot-tested on one surgeon and pathologist. A single investigator conducted all interviews by phone. Transcripts were analyzed independently by two investigators using a grounded approach,ho then compared findings to resolve differences. RESULTS: Improvements in LN staging practice may have occurred largely due to educational presentations that created awareness, and self-initiated changes undertaken by pathologists. Executives that received performance data may not have shared this with staff, and opinion leaders engaged to promote compliance may not have fulfilled their roles. Barriers to change that are potentially amenable to quality improvement included perceptions about the practice (perceived lack of evidence for the need to examine at least 12 LNs) and associated responsibilities (blaming other profession), technical issues (need for pathology assistants, better clearing solutions and laboratory facilities), and a lack of organizational support for multidisciplinary interaction (little communication between surgeons and pathologists) or quality improvement (no change leaders or capacity for monitoring). CONCLUSION: Use of an exploratory approach provided an in-depth view of the way that numerous factors amenable to quality improvement influenced the adoption of new CRC LN staging recommendations. Continued interventions targeting physicians and executives, in the absence of a receptive organizational infrastructure, may be fruitless. Individualized rather than regional or punitive performance data, coupled with increased organizational capacity for change may stimulate greater surgical and organizational response to quality improvement. Descriptive or experimental studies are needed to test these hypotheses.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".