***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
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    ***EXAMPLES*** 
    Input: BACKGROUND The age-specific mortality rates and total deaths from specific cancers have not been documented for the various regions and sub population s of India . We therefore assessed the cause of death in 2001 - 03 in homes in small areas that were chosen to be representative of all the parts of India . METHODS At least 130 trained physicians  independently assigned causes to 122,429 deaths , which occurred in 1·1 million homes in 6671 small areas that were r and omly selected to be representative of all of India , based on a structured non-medical surveyor 's field report . FINDINGS 7137 of 122,429 study deaths were due to cancer , corresponding to 556,400 national cancer deaths in India in 2010 . 395,400 ( 71 % ) cancer deaths occurred in people aged 30 - 69 years ( 200,100 men and 195,300 women ) . At 30 - 69 years , the three most common fatal cancers were oral ( including lip and pharynx , 45,800 [ 22·9 % ] ) , stomach ( 25,200 [ 12·6 % ] ) , and lung ( including trachea and larynx , 22,900 [ 11·4 % ] ) in men , and cervical ( 33,400 [ 17·1 % ] ) , stomach ( 27,500 [ 14·1 % ] ) , and breast ( 19,900 [ 10·2 % ] ) in women . Tobacco-related cancers represented 42·0 % ( 84,000 ) of male and 18·3 % ( 35,700 ) of female cancer deaths and there were twice as many deaths from oral cancers as lung cancers . Age-st and ardised cancer mortality rates per 100,000 were similar in rural ( men 95·6 [ 99 % CI 89·6 - 101·7 ] and women 96·6 [ 90·7 - 102·6 ] ) and urban areas ( men 102·4 [ 92·7 - 112·1 ] and women 91·2 [ 81·9 - 100·5 ] ) , but varied greatly between the states , and were two times higher in the least educated than in the most educated adults ( men , illiterate 106·6 [ 97·4 - 115·7 ] vs most educated 45·7 [ 37·8 - 53·6 ] ; women , illiterate 106·7 [ 99·9 - 113·6 ] vs most educated 43·4 [ 30·7 - 56·1 ] ) . Cervical cancer was far less common in Muslim than in Hindu women ( study deaths 24 , age-st and ardised mortality ratio 0·68 [ 0·64 - 0·71 ] vs 340 , 1·06 [ 1·05 - 1·08 ] ) . INTERPRETATION Prevention of tobacco-related and cervical cancers and earlier detection of treatable cancers would reduce cancer deaths in India , particularly in the rural areas that are underserved by cancer services . The substantial variation in cancer rates in India suggests other risk factors or causative agents that remain to be discovered . FUNDING Bill & Melinda Gates Foundation and US National Institutes of Health BACKGROUND National malaria death rates are difficult to assess because reliably diagnosed malaria is likely to be cured , and deaths in the community from undiagnosed malaria could be misattributed in retrospective enquiries to other febrile causes of death , or vice-versa . We aim ed to estimate plausible ranges of malaria mortality in India , the most populous country where the disease remains common . METHODS  Full-time non-medical field workers interviewed families or other respondents about each of 122,000 deaths during 2001 - 03 in 6671 r and omly selected areas of India , obtaining a half-page narrative plus answers to specific questions about the severity and course of any fevers . Each field report was sent to two of 130 trained physicians , who independently coded underlying causes , with discrepancies resolved either via anonymous reconciliation or adjudication . FINDINGS Of all coded deaths at ages 1 month to 70 years , 2681 ( 3·6 % ) of 75,342 were attributed to malaria . Of these , 2419 ( 90 % ) were in rural areas and 2311 ( 86 % ) were not in any health-care facility . Death rates attributed to malaria correlated geographically with local malaria transmission ratesderived independently from the Indian malaria control programme . The adjudicated results show 205,000 malaria deaths per year in India before age 70 years ( 55,000 in early childhood , 30,000 at ages 5 - 14 years , 120,000 at ages 15 - 69 years ) ; 1·8 % cumulative probability of death from malaria before age 70 years . Plausible lower and upper bounds ( on the basis of only the initial coding ) were 125,000 - 277,000 . Malaria accounted for a substantial minority of about 1·3 million unattended rural fever deaths attributed to infectious diseases in people younger than 70 years . INTERPRETATION Despite uncertainty as to which unattended febrile deaths are from malaria , even the lower bound greatly exceeds the WHO estimate of only 15,000 malaria deaths per year in India ( 5000 early childhood , 10 000 thereafter ) . This low estimate should be reconsidered , as should the low WHO estimate of adult malaria deaths worldwide . FUNDING US National Institutes of Health , Canadian Institute of Health Research , Li Ka Shing Knowledge Institute Background Physician-coded verbal autopsy ( PCVA ) is the most widely used method to determine causes of death ( CODs ) in countries where medical certification of death is uncommon . Computer-coded verbal autopsy ( CCVA ) methods have been proposed as a faster and cheaper alternative to PCVA , though they have not been widely compared to PCVA or to each other . Methods We compared the performance of open- source r and om forest , open- source tariff method , InterVA-4 , and the King-Lu method to PCVA on five data sets comprising over 24,000 verbal autopsies from low- and middle-income countries . Metrics to assess performance were positive predictive value and partial chance-corrected concordance at the individual level , and cause-specific mortality fraction accuracy and cause-specific mortality fraction error at the population level . Results The positive predictive value for the most probable COD predicted by the four CCVA methods averaged about 43 % to 44 % across the data sets . The average positive predictive value improved for the top three most probable CODs , with greater improvements for open- source r and om forest ( 69 % ) and open- source tariff method ( 68 % ) than for InterVA-4 ( 62 % ) . The average partial chance-corrected concordance for the most probable COD predicted by the open- source r and om forest , open- source tariff method and InterVA-4 were 41 % , 40 % and 41 % , respectively , with better results for the top three most probable CODs . Performance generally improved with larger data sets . At the population level , the King-Lu method had the highest average cause-specific mortality fraction accuracy across all five data sets ( 91 % ) , followed by InterVA-4 ( 72 % across three data sets ) , open- source r and om forest ( 71 % ) and open- source tariff method ( 54 % ) . Conclusions On an individual level , no single method was able to replicate the physician assignment of COD more than about half the time . At the population level , the King-Lu method was the best method to estimate cause-specific mortality fractions , though it does not assign individual CODs . Future testing should focus on combining different computer-coded verbal autopsy tools , paired with PCVA strengths . This includes using open- source tools applied to larger and varied data sets ( especially those including a r and om sample of deaths drawn from the population ) , so as to establish the performance for age- and sex-specific CODs Background Computer-coded verbal autopsy ( CCVA ) is a promising alternative to the st and ard approach of physician-certified verbal autopsy ( PCVA ) , because of its high speed , low cost , and reliability . This study introduces a new CCVA technique and vali date s its performance using defined clinical diagnostic criteria as a gold st and ard for a multisite sample of 12,542 verbal autopsies ( VAs ) . Methods The R and om Forest ( RF ) Method from machine learning ( ML ) was adapted to predict cause of death by training r and om forests to distinguish between each pair of causes , and then combining the results through a novel ranking technique . We assessed quality of the new method at the individual level using chance-corrected concordance and at the population level using cause-specific mortality fraction ( CSMF ) accuracy as well as linear regression . We also compared the quality of RF to PCVA for all of these metrics . We performed this analysis separately for adult , child , and neonatal VAs . We also assessed the variation in performance with and without household recall of health care experience ( HCE ) . Results For all metrics , for all setting s , RF was as good as or better than PCVA , with the exception of a nonsignificantly lower CSMF accuracy for neonates with HCE information . With HCE , the chance-corrected concordance of RF was 3.4 percentage points higher for adults , 3.2 percentage points higher for children , and 1.6 percentage points higher for neonates . The CSMF accuracy was 0.097 higher for adults , 0.097 higher for children , and 0.007 lower for neonates . Without HCE , the chance-corrected concordance of RF was 8.1 percentage points higher than PCVA for adults , 10.2 percentage points higher for children , and 5.9 percentage points higher for neonates . The CSMF accuracy was higher for RF by 0.102 for adults , 0.131 for children , and 0.025 for neonates . Conclusions We found that our RF Method outperformed the PCVA method in terms of chance-corrected concordance and CSMF accuracy for adult and child VA with and without HCE and for neonatal VA without HCE . It is also preferable to PCVA in terms of time and cost . Therefore , we recommend it as the technique of choice for analyzing past and current verbal autopsies Background Physician review of a verbal autopsy ( VA ) and completion of a death certificate remains the most widely used approach for VA analysis . This study provides new evidence about the performance of physician-certified verbal autopsy ( PCVA ) using defined clinical diagnostic criteria as a gold st and ard for a multisite sample of 12,542 VAs . The study was also design ed to analyze issues related to PCVA , such as the impact of a second physician reader on the cause of death assigned , the variation in performance with and without household recall of health care experience ( HCE ) , and the importance of local information for physicians reading VAs . Methods The certification was performed by 24 physicians . The assignment of VA was r and om and blinded . Each VA was certified by one physician . Half of the VAs were review ed by a different physician with household recall of health care experience included . The completed death certificate was processed for automated ICD-10 coding of the underlying cause of death . PCVA was compared to gold st and ard cause of death assignment based on strictly defined clinical diagnostic criteria that are part of the Population Health Metrics Research Consortium ( PHMRC ) gold st and ard verbal autopsy study . Results For individual cause assignment , the overall chance-corrected concordance for PCVA  against the gold st and ard cause of death is less than 50 % , with substantial variability by cause and physician . Physicians assign the correct cause around 30 % of the time without HCE , and addition of HCE improves performance in adults to 45 % and slightly higher in children to 48 % . Physicians estimate cause-specific mortality fractions ( CSMFs ) with considerable error for adults , children , and neonates . Only for neonates for a cause list of six causes with HCE is accuracy above 0.7 . In all three age groups , CSMF accuracy improves when household recall of health care experience is available . Conclusions Results show that physician coding for cause of death assignment may not be as robust as previously thought . The time and cost required to initially collect the verbal autopsies must be considered in addition to the analysis , as well as the impact of diverting physicians from servicing immediate health needs in a population to review VAs . All of these considerations highlight the importance and urgency of developing better methods to more reliably analyze past and future verbal autopsies to obtain the highest quality mortality data from population s without reliable death certification Background Verbal autopsy is important for detecting causes of death including HIV in areas with inadequate vital registration systems . Before antiretroviral therapy ( ART ) introduction , a  verbal autopsy study in rural Ug and a found that half of adult deaths assessed were in HIV-positive individuals . We used verbal autopsy to compare the proportion of HIV-positive adult deaths in the periods before and after ART introduction . Methods Between 2006 and 2008 , all adult ( ≥ 13 years ) deaths in a prospect i ve population -based cohort study were identified by monthly death registration , and HIV serostatus was determined through annual serosurveys . A clinical officer interviewed a relative of the deceased using a verbal autopsy question naire . Two clinicians independently review ed the question naires and classified the deaths as HIV-positive or not . A third clinician was the tie-breaker in case of nonagreement . The performance of the verbal autopsy tool was assessed using HIV serostatus as the gold st and ard of comparison . We 
    Output:
    
Sensitivity of PCVA versus hospital-assigned COD varied widely by cause , but showed consistently high specificity .
At the population level , the relative CSMF error between PCVA and hospital-based deaths indicated good performance for most CODs .
R and om Forest had the best CSMF accuracy performance , followed closely by PCVA and the other CCVA methods , but with lower values for InterVA-3 .
Conclusions There is no single best-performing coding method for verbal autopsies across various studies and metrics .
There is little current justification for CCVA to replace PCVA , particularly as physician diagnosis remains the worldwide st and ard for clinical diagnosis on live patients .