Symptom appraisal, help seeking, and lay consultancy for symptoms of head and neck cancer
Bibliographic record
Abstract
OBJECTIVE: Early diagnosis is important in head and neck cancer (HNC) patients to maximize the effectiveness of the treatments and minimize the debilitation associated with both the cancer and the invasive treatments of advanced disease. Many patients present with advanced disease, and there is little understanding as to why. This study investigated patients' symptom appraisal, help seeking, and lay consultancy up to the time they first went to see a health care professional (HCP). METHODS: We interviewed 83 patients diagnosed with HNC. The study design was cross sectional and consisted of structured telephone interviews and a medical chart review. We gathered information on the participant's personal reactions to their symptoms, characteristics of their social network, and the feedback they received. RESULTS: We found that 18% of the participants thought that their symptoms were urgent enough to warrant further investigation. Participants rarely (6%) attributed their symptoms to cancer. Eighty-nine percent reported that they were unaware of the early warning signs and symptoms of HNC. Fifty-seven percent of the participants disclosed their symptoms to at least one lay consultant before seeking help from an HCP. The lay consultants were usually their spouse (77%), and the most common advice they offered was to see a doctor (76%). Lastly, 81% of the participants report that their spouse influenced their decision to see an HCP. CONCLUSIONS: The results of this study suggest that patients frequently believe that their symptoms were nonurgent and that their lay consultants influence their decision to seek help from an HCP.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".