Expresión numérica del curso clínico de la enfermedad. Manejo de datos.
Saved in:
| Title: | Expresión numérica del curso clínico de la enfermedad. Manejo de datos. |
|---|---|
| Alternate Title: | Numerical expression of the clinical course of the disease. Data management. |
| Authors: | Talavera, Juan Osvaldo1 jotalaverap@abchospital.com, Roy-García, Ivonne2, Díaz-Torres, Sofía Teresa1, Palacios-Cruz, Lino3, Noguez-Ramos, Alejandro1, Pérez-Rodríguez, Marcela2, Ángel Martínez, Miguel1, Silva-Guzmán, Jessica E.1, Rivas-Ruiz, Rodolfo2 |
| Source: | Revista Medica del IMSS. 2023 Supplement 2, pS503-S509. 7p. |
| Subjects: | DATA management, DATA quality, ELECTRONIC data processing, DATA scrubbing, DISEASE progression |
| Abstract (English): | Data management "behind the scenes" refers to collection, cleaning, imputation, and demarcation; and despite of being indispensable processes, they are usually neglected and thus, generate erroneous information. During the collection are errors: omission of covariates, deviation from the objective, and insufficient quality. The omission of covariates distorts the result attributed to the main manoeuvre. Deviation from the primary objective commonly occurs when the outcome is rare, delayed, or subjective and promotes substitution by nonequivalent surrogate variables. Moreover, insufficient quality occurs due to inadequate instruments, omission of the measurement procedure, or measurements out of context, such as attribution at the wrong time or equivalent. Furthermore, cleaning implies identifying erroneous, extreme, and missing values, which may or may not be imputed, depending on the percentage. The values of the manoeuvre or the outcome are never imputed, nor are patients eliminated due to a lack of values. Finally, the demarcation of each variable seeks to give it a clinical meaning about the outcome, for which a hierarchical sequence of criteria is followed: 1) previous clinical study, 2) expert agreement, 3) clinical judgment of the investigator/investigators, and 4) statistics. Acting without quality controls in data management frequently causes involuntary lies and confuses instead of clarifying. [ABSTRACT FROM AUTHOR] |
| Abstract (Spanish): | El manejo de datos "tras bambalinas" se refiere a los procesos de recopilación, limpieza, imputación y demarcación; los cuales, aun siendo indispensables, usualmente suelen ser descuidados, por lo que generan información errónea. Durante la recopilación son errores: omisión de covariables, desvío del objetivo, y calidad insuficiente. La omisión de covariables distorsiona el resultado atribuido a la maniobra principal. El desvío del objetivo primario es común cuando el desenlace es raro, tardado o subjetivo y promueve la sustitución por variables subrogadas no equivalentes. Además, la calidad insuficiente, sucede por instrumentos inadecuados, omisión del procedimiento de medición, o medición fuera de contexto -como atribución a destiempo o equivalente-. Por otro lado, la limpieza implica identificar valores erróneos, extremos y faltantes, que podrán ser o no imputados, dependiendo del porcentaje se imputará comúnmente por la medida de resumen. Nunca se imputan los valores de la maniobra ni del desenlace, ni se eliminan pacientes por falta de valores. Finalmente, la demarcación de cada variable busca un significado clínico en referencia al desenlace, para ello se sigue una secuencia jerárquica de criterios: 1) estudio clínico previo, 2) acuerdo de expertos, 3) juicio clínico del investigador/investigadores y 4) estadística. Actuar sin controles de calidad en el manejo de datos provoca frecuentemente mentiras involuntarias y confunde en lugar de esclarecer. [ABSTRACT FROM AUTHOR] |
| Copyright of Revista Medica del IMSS is the property of Direccion de Prestaciones Medicas - IMSS and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | MedicLatina |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
|---|---|
| Header | DbId: lth DbLabel: MedicLatina An: 193079928 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Expresión numérica del curso clínico de la enfermedad. Manejo de datos. – Name: TitleAlt Label: Alternate Title Group: TiAlt Data: Numerical expression of the clinical course of the disease. Data management. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Talavera%2C+Juan+Osvaldo%22">Talavera, Juan Osvaldo</searchLink><relatesTo>1</relatesTo><i> jotalaverap@abchospital.com</i><br /><searchLink fieldCode="AR" term="%22Roy-García%2C+Ivonne%22">Roy-García, Ivonne</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Díaz-Torres%2C+Sofía+Teresa%22">Díaz-Torres, Sofía Teresa</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Palacios-Cruz%2C+Lino%22">Palacios-Cruz, Lino</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Noguez-Ramos%2C+Alejandro%22">Noguez-Ramos, Alejandro</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Pérez-Rodríguez%2C+Marcela%22">Pérez-Rodríguez, Marcela</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Ángel+Martínez%2C+Miguel%22">Ángel Martínez, Miguel</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Silva-Guzmán%2C+Jessica+E%2E%22">Silva-Guzmán, Jessica E.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Rivas-Ruiz%2C+Rodolfo%22">Rivas-Ruiz, Rodolfo</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Revista+Medica+del+IMSS%22">Revista Medica del IMSS</searchLink>. 2023 Supplement 2, pS503-S509. 7p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22DATA+management%22">DATA management</searchLink><br /><searchLink fieldCode="DE" term="%22DATA+quality%22">DATA quality</searchLink><br /><searchLink fieldCode="DE" term="%22ELECTRONIC+data+processing%22">ELECTRONIC data processing</searchLink><br /><searchLink fieldCode="DE" term="%22DATA+scrubbing%22">DATA scrubbing</searchLink><br /><searchLink fieldCode="DE" term="%22DISEASE+progression%22">DISEASE progression</searchLink> – Name: Abstract Label: Abstract (English) Group: Ab Data: Data management "behind the scenes" refers to collection, cleaning, imputation, and demarcation; and despite of being indispensable processes, they are usually neglected and thus, generate erroneous information. During the collection are errors: omission of covariates, deviation from the objective, and insufficient quality. The omission of covariates distorts the result attributed to the main manoeuvre. Deviation from the primary objective commonly occurs when the outcome is rare, delayed, or subjective and promotes substitution by nonequivalent surrogate variables. Moreover, insufficient quality occurs due to inadequate instruments, omission of the measurement procedure, or measurements out of context, such as attribution at the wrong time or equivalent. Furthermore, cleaning implies identifying erroneous, extreme, and missing values, which may or may not be imputed, depending on the percentage. The values of the manoeuvre or the outcome are never imputed, nor are patients eliminated due to a lack of values. Finally, the demarcation of each variable seeks to give it a clinical meaning about the outcome, for which a hierarchical sequence of criteria is followed: 1) previous clinical study, 2) expert agreement, 3) clinical judgment of the investigator/investigators, and 4) statistics. Acting without quality controls in data management frequently causes involuntary lies and confuses instead of clarifying. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Abstract (Spanish) Group: Ab Data: El manejo de datos "tras bambalinas" se refiere a los procesos de recopilación, limpieza, imputación y demarcación; los cuales, aun siendo indispensables, usualmente suelen ser descuidados, por lo que generan información errónea. Durante la recopilación son errores: omisión de covariables, desvío del objetivo, y calidad insuficiente. La omisión de covariables distorsiona el resultado atribuido a la maniobra principal. El desvío del objetivo primario es común cuando el desenlace es raro, tardado o subjetivo y promueve la sustitución por variables subrogadas no equivalentes. Además, la calidad insuficiente, sucede por instrumentos inadecuados, omisión del procedimiento de medición, o medición fuera de contexto -como atribución a destiempo o equivalente-. Por otro lado, la limpieza implica identificar valores erróneos, extremos y faltantes, que podrán ser o no imputados, dependiendo del porcentaje se imputará comúnmente por la medida de resumen. Nunca se imputan los valores de la maniobra ni del desenlace, ni se eliminan pacientes por falta de valores. Finalmente, la demarcación de cada variable busca un significado clínico en referencia al desenlace, para ello se sigue una secuencia jerárquica de criterios: 1) estudio clínico previo, 2) acuerdo de expertos, 3) juicio clínico del investigador/investigadores y 4) estadística. Actuar sin controles de calidad en el manejo de datos provoca frecuentemente mentiras involuntarias y confunde en lugar de esclarecer. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Revista Medica del IMSS is the property of Direccion de Prestaciones Medicas - IMSS and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=lth&AN=193079928 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.5281/zenodo.8319834 Languages: – Code: spa Text: Spanish PhysicalDescription: Pagination: PageCount: 7 StartPage: S503 Subjects: – SubjectFull: DATA management Type: general – SubjectFull: DATA quality Type: general – SubjectFull: ELECTRONIC data processing Type: general – SubjectFull: DATA scrubbing Type: general – SubjectFull: DISEASE progression Type: general Titles: – TitleFull: Expresión numérica del curso clínico de la enfermedad. Manejo de datos. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Talavera, Juan Osvaldo – PersonEntity: Name: NameFull: Roy-García, Ivonne – PersonEntity: Name: NameFull: Díaz-Torres, Sofía Teresa – PersonEntity: Name: NameFull: Palacios-Cruz, Lino – PersonEntity: Name: NameFull: Noguez-Ramos, Alejandro – PersonEntity: Name: NameFull: Pérez-Rodríguez, Marcela – PersonEntity: Name: NameFull: Ángel Martínez, Miguel – PersonEntity: Name: NameFull: Silva-Guzmán, Jessica E. – PersonEntity: Name: NameFull: Rivas-Ruiz, Rodolfo IsPartOfRelationships: – BibEntity: Dates: – D: 02 M: 09 Text: 2023 Supplement 2 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 04435117 Titles: – TitleFull: Revista Medica del IMSS Type: main |
| ResultId | 1 |
