Khensa ya vele i vuvabyi lebyi nga ni khombo naswona byi nga ha dlaya.
I khensa leyi tolovelekeke swinene eka vavasati emisaveni hinkwayo naswona i xivangelo xa vumbirhi lexi vangaka rifu ra vavasati hikwalaho ka khensa.
Hi ku ya hi American Cancer Society, hi 20220, ku ringanyetiwa leswaku 276,480 wa mavabyi lamantshwa ya khensa ya vele lama tlulelaka ma kumiwile eka vavasati va le United States, naswona kwalomu ka vavasati va 42,170 va le U.S. va dlayiwe hi khensa ya vele.
Hambiswiritano, nhlayo ya mafu ya khensa ya vele yi le ku hungutekeni ku sukela hi 1989, ku ve na ku hunguteka ka 40% eka nhlayo ya mafu ya khensa ya vele ku sukela hi 1989 ku ya eka 2017, ngopfu-ngopfu hikwalaho ka ku antswisiwa ka ku kuma mavabyi ka ha ri na nkarhi na vutshunguri.
Khombo ro dlayiwa hi khensa ya vele ra hambana hi ku ya hi malembe, xiyimo xa vuvabyi ni muxaka wa khensa ya vele.
Loko vuvabyi lebyi byi hatla byi voniwa ni ku tshunguriwa, munhu a nga pona.
van Schoor G, Moss SM, Otten JD, Donders R, Paap E, den Heeten GJ, Holland R, Broeders MJ, Verbeek AL: Increasingly strong reduction in breast cancer mortality due to screening. Br J Cancer. 2011, 104 (6): 910-4.
DeSantis CE, Ma J, Gaudet MM, Newman LA, Miller KD, Goding Sauer A, Jemal A, Siegel RL: Breast cancer statistics, 2019. CA Cancer J Clin. 2019, 69 (6): 438-451.
Icaza G, Núñez L, Bugueño H: [Epidemiological analysis of breast cancer mortality in women in Chile]. Rev Med Chil. 2017, 145 (1): 106-114.
Monticciolo DL, Newell MS, Hendrick RE, Helvie MA, Moy L, Monsees B, Kopans DB, Eby PR, Sickles EA: Breast Cancer Screening for Average-Risk Women: Recommendations From the ACR Commission on Breast Imaging. J Am Coll Radiol. 2017, 14 (9): 1137-1143.
Deaths from breast cancer--United States, 1991. MMWR Morb Mortal Wkly Rep. 1994, 43 (15): 273, 279-81.
Kuroishi T, Hirose K, Suzuki T, Tominaga S: Effectiveness of mass screening for breast cancer in Japan. Breast Cancer. 2000, 7 (1): 1-8.
['Xihlambanyo xa vutihlamuleri: swa vutshunguri']
['Website leyi yi endleriwe ku dyondzisa ni ku nyika rungula ntsena naswona a yi nyiki switsundzuxo swa vutshunguri kumbe mintirho ya vativi va swa vutshunguri.']
['Rungula leri nyikeriweke a ri fanelanga ri tirhisiwa ku kambela kumbe ku tshungula xiphiqo xa rihanyo kumbe vuvabyi, naswona lava lavaka xitsundzuxo xa vutshunguri va fanele va vonana ni dokodela la nga ni mpfumelelo.']
['Xiya leswaku ndlela leyi ti-neural net ti hlamulaka swivutiso ha yona a yi pakanisi ngopfu loko ku vulavuriwa hi tinomboro, to tanihi nhlayo ya vanhu lava khomiweke hi vuvabyi byo karhi.']
["Minkarhi hinkwayo kombela xitsundzuxo eka dokodela wa wena kumbe eka muongori la fanelekaka malunghana ni xiyimo xa rihanyo ra wena. U nga tshuki u honisa xitsundzuxo xa dokodela kumbe u hlwela ku xi lava hikwalaho ka leswi u swi hlayeke eka website leyi. Loko u ehleketa leswaku u le xiyin'weni xa xihatla xa rihanyo, bela riqingho eka 911 kumbe u ya ekamareni ra xihatla leri nga ekusuhi na wena hi ku hatlisa. A ku na vuxaka bya dokodela ni muvabyi lebyi tumbuluxiweke hi website leyi kumbe ku tirhisiwa ka yona. BioMedLib kumbe vatirhi va yona, kumbe un'wana ni un'wana la hoxaka xandla eka website leyi, a nga endli switiyisekiso, leswi nga erivaleni kumbe leswi nga erivaleni, malunghana ni rungula leri nga laha kumbe ku tirhisiwa ka rona."]
['Ku ala ku byarha vutihlamuleri: mfanelo yo tsala']
['Nawu wa Digital Millennium Copyright Act wa 1998, 17 U.S.C. § 512 (DMCA) wu nyika tindlela ta ku tihlanganisa na vini va timfanelo ta vuhleri lava va tshembaka leswaku swilo leswi humelelaka eka Internet swi tlula timfanelo ta vona ehansi ka nawu wa vuhleri wa U.S.']
['Loko u tshemba leswaku rungula kumbe swilo leswi kumekaka eka website ya hina kumbe eka mintirho ya hina swi tlula mfanelo ya wena yo endla swilo, wena (kumbe muyimeri wa wena) u nga hi rhumela xitiviso u kombela leswaku rungula kumbe swilo leswi swi susiwa kumbe swi siveriwa.']
['Switiviso swi fanele ku rhumeriwa hi ku tsala hi imeyili (languta eka xiyenge xa "Vuxaka" eka adirese ya imeyili).']
["DMCA yi lava leswaku xitiviso xa wena xa ku tlula nawu wa mfanelo ya ku tsala xi katsa mahungu lawa landzelaka: (1) nhlamuselo ya ntirho lowu nga na mfanelo ya ku tsala lowu ku vuriwaka leswaku wu tluriwile; (2) nhlamuselo ya leswi ku vuriwaka leswaku swi tlula nawu wa ku tsala ni mahungu lama ringaneke ku hi pfumelela ku kuma leswi nga endzeni; (3) mahungu ya ku tihlanganisa na wena, ku katsa ni adirese ya wena, nomboro ya riqingho na adirese ya imeyili; (4) xitiviso xa wena xa leswaku u ni ripfumelo ra leswaku leswi nga endzeni hi ndlela leyi ku vilelaka ha yona a swi pfumeleriwanga hi n'wini wa mfanelo ya ku tsala, kumbe muyimeri wa yena, kumbe hi ku tirha ka nawu wihi na wihi; "]
['(5) xitiyisiso xa wena, lexi sayiniweke ehansi ka nxupulo wa ku hemba, xa leswaku mahungu lama nga eka xitiviso i ntiyiso ni leswaku u na matimba yo tirhisa timfanelo ta vuqambi leti ku vuriwaka leswaku ti tluriwile;']
["na (6) ku sayina ka xiviri kumbe ka elektroniki ka n'wini wa mfanelo ya vuqambi kumbe munhu la pfumeleriweke ku endla hi vito ra n'wini wa mfanelo ya vuqambi. "]
['Loko u nga nghenisi vuxokoxoko hinkwabyo lebyi nga laha henhla swi nga endla leswaku ku tirhana ni xivilelo xa wena swi hlwela.']
['Ku Tihlanganisa']
['Hi kombela u hi rhumela imeyili hi xivutiso/xiringanyeto xihi na xihi.']
How deadly is breast cancer?
Breast cancer is a serious and potentially deadly disease.
It is the most common cancer among women worldwide and the second leading cause of cancer death in women.
According to the American Cancer Society, in 20220, an estimated 276,480 new cases of invasive breast cancer were diagnosed in women in the United States, and about 42,170 women in the U.S. died from breast cancer.
However, the death rate from breast cancer has been declining since 1989, with a 40% decrease in the breast cancer death rate from 1989 to 2017, largely due to improvements in early detection and treatment.
The risk of dying from breast cancer varies depending on factors such as age, stage at diagnosis, and the type of breast cancer.
Early detection and treatment can significantly improve the chances of survival.
Disclaimer: medical
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Please note the neural net that generates answers to the questions, is specially inaccurate when it comes to numeric content. For example, the number of people diagnosed with a specific disease.
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Disclaimer: copyright
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['Hi ku komisa']
['BioMedLib yi tirhisa tikhompyuta leti nga lo endliwa (ti-algorithm ta michini) ku endla swivutiso ni tinhlamulo.']
['Hi sungula hi minkandziyiso ya swa vutshunguri ya 35 wa timiliyoni ya PubMed/Medline. Nakambe, matluka ya Web ya RefinedWeb.']