Colorectal Carcinoma Screening in Lagos, Nigeria, Are We Doing it Right?
Bibliographic record
Abstract
BACKGROUND: Screening for colorectal cancer (CRC) has proven effective in reducing disease mortality and is also cost effective. Recent reports indicate that colorectal cancer is not uncommon and presents with advanced disease in Nigeria. Thus this study was aimed at reviewing the practice of CRC screening among medical practitioners in Nigeria. METHODS: A self-administered questionnaire was utilized to obtain data for this study, which was distributed to over 500 practising doctors in Lagos, Nigeria from September to November 2007. The data obtained from the questionnaire include basic demographics, type of practice, duration in years of medical practice described as short (≤ 5 years), medium (5 to 10 years) or long (> 10 years), and knowledge regarding CRC, as well as CRC screening techniques and methodologies. RESULTS: There were 300 respondents with a mean age (SD) of 33 (7.8) years and an age range of 23 - 67 years. In terms of duration of medical practice, 190 (63%) were short, 43 (14%) medium and 67 (23%) long. Majority (65%) of the respondents were in teaching hospitals, 18.5% in private hospitals and 5.7% were in general (community) hospitals. The knowledge of the clinical features as well as the risk factors of CRC was fair in over 75% of the respondents. Most respondents, 265 (87.8%), agreed that CRC was worth screening for; 21 (5%) did not. In all, 246 (82%) gave reasons for their responses. However, just over half of the respondents employed one of the following: faecal occult blood test (FOBT), double contrast barium enema (DCBE), flexible sigmoidoscopy, colonoscopy, or a combination of any of the techniques for screening. Usage of CT colonography was low. Screening rates by respondents for other malignancies in this survey was higher than that of CRC (prostate 95%, breast 97%, cervix 99%), though the most commonly encountered malignancy was breast cancer. On the contrary, for surveillance purposes, barely half of the respondents used FOBT annually or colonoscopy every 10 years, while less than half employed DCBE, sigmoidoscopy and CT colonography. CONCLUSIONS: Although awareness of CRC screening in this study is high, its performance is very low and highly variable in form in our region. There is a need to improve the practice of CRC screening through sensitising of medical practitioners to the need for screening, increase knowledge with regard to the relative merits of available methodologies for screening/surveillance of CRC and provide all necessary diagnostic resources and possible formulation of effective local guidelines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".