mission:BRAIN 🧠, Unilorin

2/28, 9:59[2/28, 9:59 PM] 

For the general review:

Firstly, I will like to commend everyone for a job well done.
However I have some corrections that I want us to take note of.

1. Always stay true to the topic being discussed. If you don’t understand the topic you can go online and learn more about it. Personally, I do this if I am working on a strange project. This will generally help with the comprehensibility of the topic as well as the message being passed.

Always stay true to the topic being discussed.

2. Your flow of ideas should be nice and detailed. You can’t just list a challenge facing something without explaining it. And on the issue of “explanation”, always cite real world examples or evidence based studies(clinical trials or original studies).

Always cite real world examples or evidence based studies(clinical trials or original studies).

3. In a LTE, a good introduction serves as a good cornerstone for the manuscript. It serves as the first thing the editor or the prospective reader will see when reading your work so it must be eye-catching.

In a LTE, a good introduction serves as a good cornerstone for the manuscript.

4. Lastly, always cite well both in-text and reference list.

July 3, 2025 (6:11 PM)

Keys to write an article or a literature review

1. Collect evidence in the form of citations
2. String them together into a story
3. Use AI tools in helping to string up a story
4. Use quill bolt for rephrasing and humanizing

24 August, 2025 (10:15 PM)

“Systematic reviews and meta-analyses represent the highest level of evidence in research, providing rigorous and unbiased summaries of existing literature.”

1. With this saying, we should always be meticulous in what we do… as any single mistake can affect the quality of the review.
2. In addition, we might be early-career researchers, but we can imitate professionals; there is no limit to what we can do in this age.
3. For instance, we are extracting data, and someone extracted data from a systematic review and meta-analysis. Imagine I didn’t notice the mistake, the implications for the whole research would have been catastrophic.
4. Some people still reported metrics like AOC, which is for accuracy in machine learning; the metric for ML in this review is (DSC, Jaccard index, Precision, Sensitivity, and Specificity). I am not a professional neurosurgeon who is versed in ML yet, but I still understand the difference by taking my time to learn about these things online.