Extent and Predictors of Delays in Diagnosis of Cervical Cancer in Addis Ababa, Ethiopia: A Population-Based Prospective Study
Bibliographic record
Abstract
PURPOSE: A substantial proportion of cervical cancers are diagnosed at advanced stage in Ethiopia. Therefore, the aim of this study was to determine the extent and predictors of delays in cervical cancer diagnosis in Addis Ababa. PATIENTS AND METHODS: We prospectively recruited 231 patients with cervical cancer diagnosed from January 1, 2017, to June 30, 2018, in 7 health facilities in Addis Ababa, representing 99% of all cervical cancers recorded in the Addis Ababa population-based cancer registry. A structured questionnaire on patients' experience was administered face to face by trained interviewers. Health-seeking intervals > 90 days (date from recognition of symptoms to medical consultation) and diagnostic intervals > 30 days (dates from medical consultation to diagnostic confirmation) were categorized as delayed. Factors associated with these delays were assessed using multivariable binary logistic regression models. RESULTS: The median health-seeking and diagnostic intervals for patients with cervical cancer in Addis Ababa were 10 and 97 days, respectively. Approximately one quarter of the patients were delayed in seeking medical consultation, and three fourths of the patients had delayed diagnostic confirmation. Factors associated with health-seeking delays included poor cervical cancer awareness, practicing of religious rituals, and waiting for additional symptoms before visiting a health facility. Factors associated with diagnostic delays included first contact with primary health care units and visits to ≥ 4 different health facilities before diagnosis. CONCLUSION: A considerable proportion of patients with cervical cancer in Addis Ababa have delays in seeking medical care and diagnostic conformation. These findings reinforce the need for programs to enhance awareness about cervical cancer signs and symptoms and the importance of early diagnosis in the community and among health care providers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".