The role of annual personal health visits (APHV) on patients' with cancer knowledge and perceptions of the harms of continued smoking.
Bibliographic record
Abstract
152 Background: With improvements in cancer detection and therapies, important secondary prevention measures in survivorship include smoking cessation. Primary care providers have an opportunity to discuss these measures with cancer survivors at APHV. We evaluated whether having a recent APHV is associated with cancer patients’ awareness and perceptions of the harms of continued smoking. Methods: Cancer survivors were surveyed from April 2014 to May 2016 with respect to their smoking history, knowledge and perceptions of the harms of continued smoking along with the date of their most recent APHV (term changed from annual health physical examination in 2013). Multivariable logistic regression analyses assessed the association of having an APHV with knowledge and perceptions of the harms of continued smoking. Results: Of 985 cancer patients, 23% smoked at diagnosis; 34% quit > 1 year prior to diagnosis; 55% had tobacco-related cancers; 77% received curative therapy. From a knowledge viewpoint, over 52% reported being unaware that smoking negatively impacts cancer outcomes; despite this, most perceived smoking to negatively influence quality of life (75%), survival (76%), and fatigue (73%). Within the last year, 48% had an APHV, while 84% had an APHV at any time in the past; 18 (2%) reported not having a family doctor. Patients who had an APHV in the last year were more likely to be aware that continued smoking can increase risk of death (adjusted odds ratio (aOR)=1.49, 95% CI [1.13-1.96], P=0.004), and more likely to perceive smoking to negatively impact quality of life (aOR=1.37 [0.94-1.99], P=0.10), survival (aOR=1.60 [0.95-2.71], P=0.08), and fatigue (aOR=1.63 [1.11-2.39], P=0.01). Those ever having an APHV were more likely aware that smoking can increase risk of death (aOR=1.61 [1.07-2.43], P=0.02) and second primaries (aOR=1.53 [1.02-2.33], P=0.04). Conclusions: Having a recent APHV was associated with improved awareness and perceptions of the harms of continued smoking, but it is unclear whether this is related to provider counseling or a healthy bias effect. APHV may be an appropriate time for primary care providers to treat tobacco addiction in their cancer survivors.
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".