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Record W2910556076 · doi:10.1177/2057178x18818661

A revisit at 16 years for individuals from peri-urban New Delhi for tobacco use and associated oral lesions

2019· article· en· W2910556076 on OpenAlexaff
Ravi Mehrotra, Suzanne Tanya Nethan, Priyanka Ravi, Shekhar Grover, Shashi Sharma, GK Rath, Jasbir Kaur, Ranju Ralhan, Anurag Srivastava

Bibliographic record

VenueTranslational Research in Oral Oncology · 2019
Typearticle
Languageen
FieldDentistry
TopicOral Health Pathology and Treatment
Canadian institutionsMount Sinai Hospital
FundersIndian Council of Medical Research
KeywordsMedicineTobacco useIncidence (geometry)DemographicsChewing tobaccoLesionCancerPopulationInternal medicineEnvironmental healthDemographySurgery

Abstract

fetched live from OpenAlex

Objectives: India has a high incidence of oral cancer due to multifarious tobacco use. The objective of this study was to assess the status of tobacco-related oral lesions over 16 years, in a screen-detected population. Methods: This cross-sectional study involved home visits of 2000 Delhi residents, previously screened for oral potentially malignant disorders/oral cancer and counseled for tobacco cessation. Their basic demographics and tobacco/alcohol history were noted followed by oral visual examination for any related mucosal abnormalities. The data thus obtained were statistically analyzed. Results: Two hundred and sixty-five individuals (13.2%) could be traced after 16 years. The status of oral lesions varied across the participants, mainly in terms of their location, type, number, and/or presence/absence; no oral malignancies were noted. Most individuals had either a decreased use (34%, p < 0.001) or had quit tobacco (25.7%, p < 0.001); 8.3% individuals from the former and 5.7% from the latter group showed complete lesion(s) regression. The overall change in the tobacco use and oral lesions showed a highly significant positive association ( p < 0.05). Conclusion: A direct relationship exists between tobacco use and oral lesions. Repeated, tobacco cessation counseling provided by health-care professionals is effective. Oral screening of high-risk individuals, along with tobacco cessation, is thus essential.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.788

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.296
GPT teacher head0.499
Teacher spread0.203 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2019
Admission routes1
Has abstractyes

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