Head and neck cancer in primary care: presenting symptoms and the effect of delayed diagnosis of cancer cases
Bibliographic record
Abstract
BACKGROUND: Little is known about the diagnosis of head and neck carcinoma in primary care. We sought to estimate the general prevalence of symptoms reported by patients with head and neck carcinomas and to determine the association between detection patterns of head and neck cancer cases in primary care and survival. METHODS: In a cross-sectional survey, we used a questionnaire to estimate the general prevalence of symptoms associated with head and neck cancer from a sample of 5646 primary care visits in 25 randomly selected health centres over 4 weeks throughout Finland. A population-based retrospective cohort study involved the 221 patients resident in one primary health care district (population about 700,000) in whom head and neck carcinoma was diagnosed between Jan. 1, 1986, and Dec. 31, 1996. Data on the initial primary care visit, clinical characteristics and survival were obtained from patient charts. RESULTS: Of 5646 visits to a primary care practitioner, 11% (617) were made because of the same symptoms as those initially reported by patients later found to have head and neck cancer. According to the cohort data, the detection rate of these carcinomas in primary care was 1 per 63,000 visits. At the initial visit of 221 patients later found to have cancer, 56% (123) received referrals, 24% (53) follow-up appointments and 20% (45) neither ("overlooked"). At 3 years, the risk of death was significantly higher among patients whose disease was overlooked (adjusted hazard ratio [HR] 1.89, 95% confidence interval [CI] 1.03-3.45). The excess risk associated with being overlooked, however, was confined to subjects with tongue or glottic tumours (HR 4.25, 95% CI 1.59- 11.4) (number needed to harm 3.0, 95% CI 1.9-6.7). INTERPRETATION: Despite the rarity of patients with head and neck carcinoma in primary care, patients with symptoms of these diseases and especially with symptoms of tongue and glottic carcinomas should be initially referred for further care or followed up.
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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 | 0.002 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".