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Record W3118604299 · doi:10.2196/23437

Evaluation of the Prevalence, Regional Phenotypic Variation, Comorbidities, Risk Factors, and Variations in Response to Different Therapeutic Modalities Among Indian Women: Proposal for the Indian Council of Medical Research–Polycystic Ovary Syndrome (ICMR–PCOS) Study

2021· article· en· W3118604299 on OpenAlexvenueno aff
Mohd Ashraf Ganie, Subhankar Chowdhury, Vanita Suri, Beena Joshi, Prasanta Kumar Bhattacharya, Sarita Agrawal, Neena Malhotra, Rakesh Sahay, Roya Rozati, Puthiyaveettil Khadar Jabbar, Vishnubhatla Sreenivas, Mukesh K. Sriwastva, Imtiyaz Ahmad Wani, Shalini Singh, Radhey Shyam Sharma

Bibliographic record

VenueJMIR Research Protocols · 2021
Typearticle
Languageen
FieldMedicine
TopicOvarian function and disorders
Canadian institutionsnot available
Fundersnot available
KeywordsPolycystic ovaryEpidemiologyMedicineSample size determinationModalitiesTherapeutic modalitiesDemographyEnvironmental healthObesityPhysical therapyPathologySocial science

Abstract

fetched live from OpenAlex

BACKGROUND: There is scanty data in India on polycystic ovary syndrome (PCOS) from several small, undersized, convenience-based studies employing differing diagnostic criteria and reporting varied regional prevalence. It is difficult to draw clear-cut conclusions from these studies; therefore, the present multicentric, well-designed, large-scale representative countrywide epidemiological study on PCOS across India was conceived with the aim to generate the actual prevalence rates of PCOS in India with a total sample size of approximately 9000 individuals. OBJECTIVE: The primary objectives of the study are to estimate the national prevalence of PCOS in India and the burden of comorbidities and to compare the variation in efficacy of standard therapeutic modalities for metabolic dysfunction in women with PCOS. METHODS: This multicentric umbrella study consists of three different substudies. Substudy 1 will involve recruitment of women aged 18-40 years using a multistage sampling technique from randomly selected polling booths across urban and rural areas to estimate national prevalence, phenotypic variation, and risk factors among regions. Substudy 2 involves recruitment of subjects from the community pool of substudy 1 and the institutional pool for quantitation of comorbidities among women with PCOS. Substudy 3, an interventional part of the study, aims for comparison of variation in efficacies of common treatment modalities and will be conducted only at 2 centers. The eligible consenting women will be randomized in a 1:1 ratio into 2 arms through a blinding procedure. All these women will undergo clinical, biochemical, and hormonal assessment at baseline and at 3 and 6 months. The data generated will be analyzed using the reliable statistical software SPSS (version 26). RESULTS: The study is ongoing and is likely to be completed by April 2022. The data will be compiled and analyzed, and the results of the study will be disseminated through publications. CONCLUSIONS: The Indian Council of Medical Research-PCOS study is the first of its kind attempting to provide accurate and comprehensive data on prevalence of PCOS in India. TRIAL REGISTRATION: Clinical Trials Registry-India CTRI/2018/11/016252; ctri.nic.in/Clinicaltrials/pmaindet2.php?trialid=26366. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/23437.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.038
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0380.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0040.006
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0040.003
Research integrity0.0010.002
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 head0.277
GPT teacher head0.470
Teacher spread0.193 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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

Citations15
Published2021
Admission routes1
Has abstractyes

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