DEFINING LUNG CANCER DIAGNOSTIC PATHWAYS IN THE PRIMARY CARE SETTING IN MONTRÉAL, QUÉBEC
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer-specific mortality in Canada, with the highest mortality observed in Québec. Incidence rates for lung cancer peak at 80–84 years of age with a median age at diagnosis of 71. Thus, the death toll due to lung cancer is concentrated among the elderly. The five-year survival rate for lung cancer is a dismal 17%. This is because lung cancers are often diagnosed at a late stage of disease when treatment options are limited. Interventions to reduce delays in diagnosis require an examination of diagnostic pathways and an understanding of the factors that influence these pathways. As patients in Canada must present in primary care before being referred to specialist care, the primary care interval within the larger diagnostic interval is a fundamental component of the diagnostic pathway. This study aims to examine lung cancer diagnostic trajectories in the primary care setting, and explore patient, disease, and health-care system factors that contribute to the diagnostic process. An explanatory sequential mixed-methods design will be employed in two phases. Phase one will involve the identification of diagnostic pathways through latent profile analysis using clinical and administrative databases along with structured patient interviews, while phase two will use semi-structured patient interviews to explore contributing factors to late diagnosis. The findings from this study will provide an evidence base from which targeted interventions can be formed to reduce or eliminate unnecessary and avoidable delays in lung cancer diagnosis.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".