Mapping local evidence on early recognition and management of people with potential cancer symptoms: a narrative review
Bibliographic record
Abstract
Aim Early cancer detection has potential to improve outcomes. However, many patients in South Africa present when the disease is at an advanced stage. The World Health Organization recommends two approaches to early cancer detection: screening asymptomatic individuals, and early recognition and management of symptomatic individuals. This paper focuses on the latter. For people with potential cancer symptoms, the journey to diagnosis is complex and influenced by multiple factors. Most symptomatic people will self-present to primary health care clinics, where primary health care providers are pivotal in triage. Methods This article presents local insights into cancer awareness measurement tools: community-level cancer symptom awareness, lay beliefs, and symptom appraisal; factors influencing the journey from symptom discovery to diagnosis; primary health care provider challenges in assessing symptomatic individuals, and interventions to support symptom assessment and help-seeking. It draws on findings from the African Women Awareness of CANcer (AWACAN) project and a narrative review of relevant published articles on journeys to cancer diagnosis in SA (2013-2023). Findings Very few cancer awareness measurement tools have been locally validated, hampering comparison and limiting opportunities for intervention development and evaluation. The AWACAN study developed and validated a local cancer awareness measurement tool for breast and cervical cancer. Studies show that most people in SA need information on cancer risk, symptoms, and pathways to care. Barriers to accessing health care include financial, infrastructural, safety, stigma, and previous health facility experiences. Primary health care providers require support for symptom assessment and referral systems. There is limited local work on developing and evaluating interventions to improve timely cancer diagnosis. Conclusions This paper underscores the importance of prioritising early recognition and management of people with symptomatic cancer as part of a comprehensive cancer control plan, providing insights for improving the journey to diagnosis.
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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 | 0.008 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".