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Record W2770763795 · doi:10.18535/jmscr/v5i11.134

A Retrospective Study on Ceaserean Section in Semi Urban AreasIndications

2017· article· en· W2770763795 on OpenAlexaboutno aff
Dr A. Rajeswari

Bibliographic record

VenueJournal of Medical Science And clinical Research · 2017
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSection (typography)Advertising

Abstract

fetched live from OpenAlex

Caeserean Section is a common operative procedure in obstetric practice throughout the world to ensure a healthy outcome of the mother and new-born. The advent of modern anesthesia, antibiotics and availability of blood transfusions the indications of this ceaserean section are being continually extended. It has been reported that the implementation of modern technology in labour and neonatology unit showed the incidence of abdominal delivery further raised to prevent potentially grave foetal and maternal morbidities. The rate of Caeserean delivery in the United States has quadrupled from 5% of obstetric delivery in 1964 to more than 23% in 1991 1 .The national C -section rate of Canada was20% and Italy was 17.5% 2 . The present study to determine the incidence and evaluate its indications in the department of Obs & Gynae in GVMCH, Vellore (Govt Vellore Medical College). This is a step to find out unnecessary indications of LSCS which may in future reduce the incidence rate in the country. In India the 'C' section rate also increased up to 35%-45%. In Tamilnadu more than 90% deliveries are conducted by Govt institutions like 'cemonc' centre. Here we want to institution than Vellore in semi urban area, GVMCH is tertiary care unit all high risk and complicated cases from PHC and GH are managed by NS.

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.

How this classification was reachedexpand

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)0.127
metaresearch head score (Gemma)0.273
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1270.273
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0000.000

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 head0.564
GPT teacher head0.725
Teacher spread0.161 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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