Considerations Regarding Puberty Assessment Methodology and the Relationship between Undernutrition and Puberty among Adolescents Living in Pakistan
Bibliographic record
Abstract
Background: Knowledge regarding the timing of pubertal milestones among those living in Pakistan is sparse. Additionally, an accurate methodology for pubertal self-assessment is key to develop a more comprehensive understanding of sexual maturation on adolescent health. Such research is lacking among those living in low-and middle-income countries (LMIC) and especially for males. Objectives: The first objective of this thesis was to assess the accuracy of pubertal self-assessment. The second objective was to determine the relationships between undernutrition and the timing of pubertal milestones among adolescents living in two different settings in Pakistan – rural Matiari and an urban slum in Karachi. Methods: A systematic review with meta-analysis was conducted to assess the level of agreement between self-assessment using Tanner Stages images and clinician-based assessment. Among youth living in rural Pakistan, the level of agreement between self-assessment using a questionnaire-based tool without images with physician assessment was determined. Next, the relationship between the timing of all pubertal milestones and undernutrition among youth living in Matiari, a rural district in Pakistan was determined using cross-sectional data. Lastly, the relationship between the timing of pubertal onset and undernutrition among youth living in an urban slum in Pakistan was determined using longitudinal data. Results: Participants in rural Matiari and the Karachi slum were able to assess their pubertal development reasonably well when mapped to a three-point classification. However, when the subtitles of the five-point Tanner Stages are of interest, clinician-based assessments were superior. Participants in rural Matiari and the Karachi slum who weren’t impacted by stunting, experienced pubertal milestones consistent with LMIC estimates. However, those with stunting or thinness experienced delays in attaining pubertal milestones. A high prevalence of anemia and nutrient deficiencies were observed in both settings. Conclusions: Integrating self-assessment puberty measures into adolescent research may reveal the interconnectivity of nutrition, health, and well-being. The body of research in this thesis highlights the nutritional vulnerability of adolescents in LMIC as demonstrated by delays in puberty in stunted youth and high levels of hidden hunger.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.175 | 0.306 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".