The pattern of thyroid cancers in Nigeria: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract BackgroundThyroid cancer is the commonest endocrine cancer. There are various histopathological types requiring different therapeutic approaches and having variable prognosis. The objective of this study was to determine the pattern of thyroid cancers in Nigeria.MethodsThe systematically searched databases were African Journal Online, Google Scholar, PubMed and SCOPUS. The pre-print databases Research Square, SciELO and medRxiv were also systematically searched. Moreover, the available grey literature was keenly searched. The meta-analysis was done using Meta XL version 5. The quality of the selected studies was assessed using the Newcastle-Ottawa scale. The DerSimonian Laird random effect model was used for the meta-analysis. Heterogeneity of the selected studies was determined using I2 statistic and the Cochran’s Q test. Publication bias was assessed with the LFK index and Doi plot.ResultsTwenty-six studies met the eligibility criteria. The total sample size was 1224. In Nigeria, thyroid cancer was most common in the 4th and 5th decades of life. It was 3.5 (95%CI: 3.0- 4.6, p<0.0001) times commoner in females than males. The pooled proportions of thyroid cancers included papillary thyroid cancer- 46% (95% CI: 40 -53), follicular thyroid cancer- 39% (95% CI: 32-45), medullary thyroid cancer – 7% (95% CI: 6 -9), and anaplastic thyroid cancer- 5% (95% CI: 3 -7). The trend showed a change from follicular cancer predominance to papillary cancer over the past two decades.ConclusionPapillary thyroid cancer is the commonest type in Nigeria. Thyroid cancer is seen more in females and it occurs most commonly in middle age.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.032 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".