J Biomed Inform - Associating co-authorship patterns with publications in high-impact journals.

Tópicos

{ research(1218) medic(880) student(794) }
{ studi(1119) effect(1106) posit(819) }
{ system(1050) medic(1026) inform(1018) }
{ inform(2794) health(2639) internet(1427) }
{ care(1570) inform(1187) nurs(1089) }
{ gene(2352) biolog(1181) express(1162) }
{ search(2224) databas(1162) retriev(909) }
{ extract(1171) text(1153) clinic(932) }
{ general(901) number(790) one(736) }
{ result(1111) use(1088) new(759) }
{ can(981) present(881) function(850) }
{ use(1733) differ(960) four(931) }
{ network(2748) neural(1063) input(814) }
{ take(945) account(800) differ(722) }
{ perform(999) metric(946) measur(919) }
{ state(1844) use(1261) util(961) }
{ data(3008) multipl(1320) sourc(1022) }
{ imag(1947) propos(1133) code(1026) }
{ measur(2081) correl(1212) valu(896) }
{ sequenc(1873) structur(1644) protein(1328) }
{ method(1219) similar(1157) match(930) }
{ patient(2315) diseas(1263) diabet(1191) }
{ studi(2440) review(1878) systemat(933) }
{ clinic(1479) use(1117) guidelin(835) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ record(1888) medic(1808) patient(1693) }
{ model(2656) set(1616) predict(1553) }
{ signal(2180) analysi(812) frequenc(800) }
{ high(1669) rate(1365) level(1280) }
{ survey(1388) particip(1329) question(1065) }
{ method(1969) cluster(1462) data(1082) }
{ model(3404) distribut(989) bayesian(671) }
{ can(774) often(719) complex(702) }
{ data(1737) use(1416) pattern(1282) }
{ system(1976) rule(880) can(841) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ imag(2675) segment(2577) method(1081) }
{ motion(1329) object(1292) video(1091) }
{ assess(1506) score(1403) qualiti(1306) }
{ treatment(1704) effect(941) patient(846) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ research(1085) discuss(1038) issu(1018) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ blood(1257) pressur(1144) flow(957) }
{ spatial(1525) area(1432) region(1030) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ patient(2837) hospit(1953) medic(668) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ cancer(2502) breast(956) screen(824) }
{ use(976) code(926) identifi(902) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

JECTIVES: To develop a method for investigating co-authorship patterns and author team characteristics associated with the publications in high-impact journals through the integration of public MEDLINE data and institutional scientific profile data.METHODS: For all current researchers at Columbia University Medical Center, we extracted their publications from MEDLINE authored between years 2007 and 2011 and associated journal impact factors, along with author academic ranks and departmental affiliations obtained from Columbia University Scientific Profiles (CUSP). Chi-square tests were performed on co-authorship patterns, with Bonferroni correction for multiple comparisons, to identify team composition characteristics associated with publication impact factors. We also developed co-authorship networks for the 25 most prolific departments between years 2002 and 2011 and counted the internal and external authors, inter-connectivity, and centrality of each department.RESULTS: Papers with at least one author from a basic science department are significantly more likely to appear in high-impact journals than papers authored by those from clinical departments alone. Inclusion of at least one professor on the author list is strongly associated with publication in high-impact journals, as is inclusion of at least one research scientist. Departmental and disciplinary differences in the ratios of within- to outside-department collaboration and overall network cohesion are also observed.CONCLUSIONS: Enrichment of co-authorship patterns with author scientific profiles helps uncover associations between author team characteristics and appearance in high-impact journals. These results may offer implications for mentoring junior biomedical researchers to publish on high-impact journals, as well as for evaluating academic progress across disciplines in modern academic medical centers.

Resumo Limpo

jectiv develop method investig coauthorship pattern author team characterist associ public highimpact journal integr public medlin data institut scientif profil datamethod current research columbia univers medic center extract public medlin author year associ journal impact factor along author academ rank department affili obtain columbia univers scientif profil cusp chisquar test perform coauthorship pattern bonferroni correct multipl comparison identifi team composit characterist associ public impact factor also develop coauthorship network prolif depart year count intern extern author interconnect central departmentresult paper least one author basic scienc depart signific like appear highimpact journal paper author clinic depart alon inclus least one professor author list strong associ public highimpact journal inclus least one research scientist department disciplinari differ ratio within outsidedepart collabor overal network cohes also observedconclus enrich coauthorship pattern author scientif profil help uncov associ author team characterist appear highimpact journal result may offer implic mentor junior biomed research publish highimpact journal well evalu academ progress across disciplin modern academ medic center

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