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Record W7033411313

Qualidade do registro das informações dos nascidos vivos de risco no município de Maringá-Paraná, no ano de 2007

2009· dissertation· en· W7033411313 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTrakya University's Institutional Open Access System (Trakya University) · 2009
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicSmart Agriculture and AI
Canadian institutionsnot available
Fundersnot available
KeywordsMedical recordQuality (philosophy)Health servicesHealth careSample (material)Data qualityRisk assessment
DOInot available

Abstract

fetched live from OpenAlex

The records produced routinely in numerous activities in the area of health are given that enable the transformation into information. The banks of data from the routine health services are used as a tool for the development of health policies, for planning and managing health services. Currently, the record is a criterion for evaluating the quality of the service where the quality of records are a reflection of the quality of care. The objective of this study was to assess the quality of information of the infants enrolled in the program for monitoring the high-risk newborns in Maringa-Parana in the various information systems in 2007. This is a crosssectional study with quantitative and analytical approach. Used as data sources SINASC, sheet Monitoring the Newborn Risk (Orange Card), Form A and C of the Information System of Primary Care and medical records of 23 Basic Health Units (UBS). Of the 4175 children born in the city of Maringa in 2007, 710 (17%) children were included in the monitoring program to high-risk newborns. Of these, we selected a random sample of 505 (71.12%) children, and 254 (50.29%) with low birth weight, 244 (48.31%) with prematurity, 142 (28.11%) children teenage mothers (<18 years), 50 (9.90%) with score &#8804; 7 and 21 (4.15%) classified as congenital anomaly. It was observed that many of these children have more of a risk criterion. Of the total of the sheets were found 131 (25.9%) children, were located 128 (25.3%) children with Chips and C with respect to records, were located 359 (71.0%). The data were analyzed by Correspondence Analysis and Binary Ascending Hierarchical Classification where the basic health units were grouped into clusters. Correspondence analysis showed that the Orange Card, the variables with significant contributions were medical (3.6) in the UBS universe, City High and Guaiapó-Requião, nursing (2.2) in the UBS High City, Guaiapó-Requião Mall, Internorte, Quebec and Alvorada III, home visits (2.3) in UBS Mandacaru, pine, South Zone and at high inclusion in other programs (2.3) in UBS Mall, Internorte, Quebec, Alvorada III; The specs for the outstanding contributions indicate that the variable with the greatest significance is the referral to dentistry (4.0) Mall and the UBS (1.5) in UBS Pinheiros and Vila Esperança Universe; in Sheet C, the relevant contributions indicate that the variables with greatest significance was the chart registration form (7.0) in UBS Mall and other variables, monitoring &#8804; 6 times the recorded (3.2) Mandacaru at UBS, for the records, the contributions combined with greater significance records were located (4.6) in UBS Acclimatization; records identified as high-risk NB (2.7) in UBS Parigot de Souza, S. Silvestre, Hope Town and Guaiapó-Requião; sheet located in Orange record (7.8) in UBS Parigot de Souza, Sao Silvestre, Vila Esperança, Guaiapó-Requião and Acclimatization; routing registered (1.8); hospital recorded (2.8) and immunization records (4.9) in UBS Mall, Falls, Industrial, strawberry, Ney Braga and universe. The quality of information in health records have a clear potential as a need for adequate health care and better organization of health services, to reclaim the basic principles of the SUS as a comprehensive care, with equity and universal access.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.680
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0020.000
Scholarly communication0.0010.004
Open science0.0060.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.274
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it