How do surgeons decide to refer patients for adjuvant cancer treatment? Protocol for a qualitative study
Bibliographic record
Abstract
BACKGROUND: Non-small cell lung cancer, breast cancer, and colorectal cancer are commonly diagnosed cancers in Canada. Patients diagnosed with early-stage non-small cell lung, breast, or colorectal cancer represent potentially curable populations. For these patients, surgery is the primary mode of treatment, with (neo)adjuvant therapies (e.g., chemotherapy, radiotherapy) recommended according to disease stage. Data from our research in Nova Scotia, as well as others', demonstrate that a substantial proportion of non-small cell lung cancer and colorectal cancer patients, for whom practice guidelines recommend (neo)adjuvant therapy, are not referred for an oncologist consultation. Conversely, surveillance data and clinical experience suggest that breast cancer patients have much higher referral rates. Since surgery is the primary treatment, the surgeon plays a major role in referring patients to oncologists. Thus, an improved understanding of how surgeons make decisions related to oncology services is important to developing strategies to optimize referral rates. Few studies have examined decision making for (neo)adjuvant therapy from the perspective of the cancer surgeon. This study will use qualitative methods to examine decision-making processes related to referral to oncology services for individuals diagnosed with potentially curable non-small cell lung, breast, or colorectal cancer. METHODS: A qualitative study will be conducted, guided by the principles of grounded theory. The study design is informed by our ongoing research, as well as a model of access to health services. The method of data collection will be in-depth, semi structured interviews. We will attempt to recruit all lung, breast, and/or colorectal cancer surgeons in Nova Scotia (n ≈ 42), with the aim of interviewing a minimum of 34 surgeons. Interviews will be audiotaped and transcribed verbatim. Data will be collected and analyzed concurrently, with two investigators independently coding and analyzing the data. Analysis will involve an inductive, grounded approach using constant comparative analysis. DISCUSSION: The primary outcomes will be (1) identification of the patient, surgeon, institutional, and health-system factors that influence surgeons' decisions to refer non-small cell lung, breast, and colorectal cancer patients to oncology services when consideration for (neo)adjuvant therapy is recommended and (2) identification of potential strategies that could optimize referral to oncology for appropriate individuals.
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 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 | 0.085 | 0.077 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.080 | 0.011 |
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".