Retrospective study of factors associated with late detection of oral cancer in alberta: A qualitative study
Bibliographic record
Abstract
Oral cancer continues to be diagnosed in advanced stages, giving patients lower chances of survival. The objective of this study was to explore reasons for delayed diagnosis of oral cancer in Alberta. A retrospective qualitative design was implemented through seven steps suggested for conducting a narrative clinical document. Data was retrieved from the Alberta Cancer Registry database between 2005 and 2017. A sample of initial consultation notes (ICN) of oral and oropharyngeal cancer patients were identified through a purposeful sampling method and added to the study until saturation was achieved. A deductive analysis approach inspired by the model pathways to treatment health care provider (HCP) was employed. From the 34 ICN included in our analysis, five main categories were identified: appraisal interval, help-seeking interval, diagnosis interval, pre-treatment interval, and other contributing factors such as health-related behaviours, system delay, and tumor characteristics. These factors negatively contributed to early detection of oral and oropharyngeal cancers and affect treatment wait time with patients, providers, and the healthcare system. Patient's lack of awareness, provider's oversight and prolonged access to care were the main reasons of delay in cancer diagnosis and management in our study. A sustainable plan for public awareness interventions and implementation of a solid curriculum for medical and dental students is needed to enhance their related knowledge, competence in clinical judgement, and treatment managements.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".