73 to 100 of children still had an Autistic Disorder diagnosis Baseline Mean Age 03 691 Cochranphosphorus parathyroid hormone and calcium and risks of poor Validate the model in other subjects not ID: 884434
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1 Baseline Mean Age 0-3- 73% to 10
Baseline Mean Age 0-3- 73% to 10
2 0% of children still had an Autist
0% of children still had an Autist
3 ic Disorder diagnosis Baseline M
ic Disorder diagnosis Baseline M
4 ean Age 0-3- =69.1%. Cochran phos
ean Age 0-3- =69.1%. Cochran phos
5 phorus, parathyroid hormone, and c
phorus, parathyroid hormone, and c
6 alcium and risks of poor Validate
alcium and risks of poor Validate
7 the model in other subjects ¥!not
the model in other subjects ¥!not
8 necessarily patients 3.!Update ex
necessarily patients 3.!Update ex
9 isting model to local situation V
isting model to local situation V
10 alidate the model in other subject
alidate the model in other subject
11 s 3.!Update existing model to loca
s 3.!Update existing model to loca
12 l situation To identify all CVD p
l situation To identify all CVD p
13 rediction models that can be appli
rediction models that can be appli
14 ed to people with type 2 diabetes
ed to people with type 2 diabetes
15 and subsequently, assess their int
and subsequently, assess their int
16 ernal and external validation, and
ernal and external validation, and
17 impact on patient outcomes ¥!Titl
impact on patient outcomes ¥!Titl
18 e: Prediction models for the risk
e: Prediction models for the risk
19 of cardiovascular disease in patie
of cardiovascular disease in patie
20 nts with type 2 Assessments are m
nts with type 2 Assessments are m
21 ade separately for different bias
ade separately for different bias
22 domains Ð!Judgment ¥!Use of scales
domains Ð!Judgment ¥!Use of scales
23 Òexplicitly discouragedÓ Ð!Weight
Òexplicitly discouragedÓ Ð!Weight
24 ing of items difficult to justify
ing of items difficult to justify
25 Ð!Often based on reporting rather
Ð!Often based on reporting rather
26 than conduct Similar approach (co
than conduct Similar approach (co
27 nsider potential biases) ¥!Differe
nsider potential biases) ¥!Differe
28 nt study types require different
nt study types require different
29 Potential biases related to: 1.!St
Potential biases related to: 1.!St
30 udy participation 2.!Study attriti
udy participation 2.!Study attriti
31 on 3.!Prognostic factor measuremen
on 3.!Prognostic factor measuremen
32 t Sample selection Recruitment me
t Sample selection Recruitment me
33 thod, ¥!Completeness of follow-up
thod, ¥!Completeness of follow-up
34 ¥!Timing of diagnosis ¥!Blinding ¥
¥!Timing of diagnosis ¥!Blinding ¥
35 !Analysis for covariates was not a
!Analysis for covariates was not a
36 ssessed as we were not investigati
ssessed as we were not investigati
37 ng predictors of outcomes. (adapt
ng predictors of outcomes. (adapt
38 ed from Hayden et al. 2006) Dist
ed from Hayden et al. 2006) Dist
39 ribution of predictors (including
ribution of predictors (including
40 missing data) for development and
missing data) for development and
41 validation data sets (if applicabl
validation data sets (if applicabl
42 e) ¥!Final and other (e.g. basic o
e) ¥!Final and other (e.g. basic o
43 r extended) multivariable models p
r extended) multivariable models p
44 resented (e.g. regression coeffici
resented (e.g. regression coeffici
45 ents, including intercept or basel
ents, including intercept or basel
46 ine hazard, model performance meas
ine hazard, model performance meas
47 ures, all with standard errors or
ures, all with standard errors or
48 Prognostic variable Outcome Anal
Prognostic variable Outcome Anal
49 ysis Subfeature Relevant domain Pa
ysis Subfeature Relevant domain Pa
50 rticipant selection In-and exclusi
rticipant selection In-and exclusi
51 on Risk prediction model ¥!Introd
on Risk prediction model ¥!Introd
52 uce from the group an example ¥!Di
uce from the group an example ¥!Di
53 scuss: Ð!Framing review question Ð
scuss: Ð!Framing review question Ð