Epidemiology of the most Prevalent Cancers in Ninewa between 2017–2021
Bibliographic record
Abstract
Purpose. Cancer is characterized by abnormal cell growth resulting from uncontrolled cell division. These cells spread and form metastatic lesions in normal tissues, leading to loss of tissue and organ function. Cancer is one of the most life-threatening diseases worldwide that develops in humans. regardless of sex, ethnicity, or nationality.Material and methods. Here, we conducted a retrospective study to collect data on the various types of cancers prevalent in Ninewa, Iraq, between 2017–2021, using hospital records pooled in the Iraq Cancer Registry. We focused on the top 10 most notable cancers prevalent in humans.Results. Our study revealed that number of novel cancer cases and mortality rates have been increasing annually between 2017– 2021. Females had higher rates of cancer occurrence than males. The most prevalent cancers in Ninewa were breast cancer in women and lung cancer in men. Leukemia was the most common pediatric cancer. Furthermore, this study reported that lung cancer had the highest mortality rate in Ninewa, followed by breast cancer.Conclusion Our study provides a statistical overview on cancer cases in the Ninewa governorate, and will be useful to clinicians, faculty members and other professionals in the medical field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".