Diagnostic sensitivity is the ability of a test to correctly generate positive result for people who actually have the disease.(True Positive rate) e.g. If sensitivity of a test is 90%, it will give positive result for 90% of people who have the disease, but will give negative results (False negative) for 10% people who actually have the disease and should have tested positive. It is calculated as Sensitivity=(True Positive/ Total positive)× 100 Total positive=(True positive+ False Neg)
Diagnostic specificity is the ability of a test to correctly generate negative results for people who actually do not have the disease.(True negative rate). Example- A test with 80% specificity, will give negative results for 80% people who actually do not have the disease.But, it will show positive results (False positive) in 20% of people, who actually do not have the disease and should have tested negative. Specificity=(TN/ Total Negative)×100, Total Negative=True negative+ False positive
Good Morning Mam, We have a new lot qc vial , After that qc runs 20 data points then calculate mean and sd value and set in instrument after that how to activity for NABL and NABH guideline? Please in Hindi Thank you
Hello mam can you take lecture on corrective action and preventive action taken when IQC is out. Also how to rectify the exact type of error and how to correct it using daily IQC graph.
Hi ma'am Kya aap bata sakte hai k 20 day iqc run karne k baad jab hum apna meen and SD ,CV,% banalete hai tab hum apne meen k according iqc range 2sd rakhe ya 3sd Kindly update as soon as possible Thanks
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What's the difference between qc and calibration ?
Very nice. Thank you. Dr. Khush Bakht Nawaz Khan from Pakistan
Welcome 🤗
Had fun learning online
Ma'am can you futher explain diagnostic sensitivity and diagnostic specificity? Didn't get it through the video.
Diagnostic sensitivity is the ability of a test to correctly generate positive result for people who actually have the disease.(True Positive rate)
e.g. If sensitivity of a test is 90%, it will give positive result for 90% of people who have the disease, but will give negative results (False negative) for 10% people who actually have the disease and should have tested positive.
It is calculated as
Sensitivity=(True Positive/ Total positive)× 100
Total positive=(True positive+ False Neg)
Diagnostic specificity is the ability of a test to correctly generate negative results for people who actually do not have the disease.(True negative rate).
Example- A test with 80% specificity, will give negative results for 80% people who actually do not have the disease.But, it will show positive results (False positive) in 20% of people, who actually do not have the disease and should have tested negative.
Specificity=(TN/ Total Negative)×100,
Total Negative=True negative+ False positive
Thanku maim,
apka lecture bahut hi achha h
Thank you so much❤
Good Morning Mam,
We have a new lot qc vial , After that qc runs 20 data points then calculate mean and sd value and set in instrument after that how to activity for NABL and NABH guideline?
Please in Hindi
Thank you
Mam also take a lecture on calibration, standards and QC
Nice and important
Very nice lecture 👍👍
Hello mam can you take lecture on corrective action and preventive action taken when IQC is out. Also how to rectify the exact type of error and how to correct it using daily IQC graph.
Wl try
Mam plasma proteins pe video banao na 🙏
Aur cardiac profile test per
Hi ma'am
Kya aap bata sakte hai k 20 day iqc run karne k baad jab hum apna meen and SD ,CV,% banalete hai tab hum apne meen k according iqc range 2sd rakhe ya 3sd
Kindly update as soon as possible
Thanks
Calculate 1SD, 2SD and 3 SD..Plot L-J chart..and use westgard rules.. In analyser if you feed values L-J chart is generated
How to know if the sample received is hemolysed or not ?
good lecture but kindly ADD subtitle on video thanks
What is quality check ?
Mam please can you give pdf of this lacture
Please iam need this file as pdf or ppt
Hindi me explain kro mem please 😢😢
Hindi me explain kro mem please 😢😢😢😢
please upload more mam