Successful implementation of a laboratory iodization quality assurance system in small-scale salt production facilities in India
Bibliographic record
Abstract
OBJECTIVE: To establish a laboratory iodization quality assurance system to support small-scale salt production facilities in India and to assess the level of agreement for the internal quality assurance (IQA) and external quality assurance (EQA) protocols. DESIGN: Operational research. The IQA and EQA programme was established in the year 2008. Agreement between field laboratories and the reference laboratory for estimation of iodine content of salt from 2008 to 2011 was assessed. Agreement was assessed using the χ 2 test, kappa statistics and the Bland-Altman plot. SETTING: Small-scale salt producers in the states of Andhra Pradesh, Gujarat, Rajasthan, Tamil Nadu, Odisha and Karnataka; 'field laboratories' supporting the small-scale salt producers; and the 'reference laboratory' of the Regional Office (South Asia) of the International Council for the Control of Iodine Deficiency Disorders. SUBJECTS: Three hundred small-scale salt producers in the states of Andhra Pradesh, Gujarat, Rajasthan, Tamil Nadu, Odisha and Karnataka and seventeen 'field laboratories'. RESULTS: A total of 6573 salt samples for IQA and 347 salt samples for EQA were exchanged between field and reference laboratories during 2008-2012. Out of the total salt sample exchanges, 527 were from Andhra Pradesh and Odisha, 2343 from Gujarat, 2016 from Rajasthan and 1677 from Tamil Nadu and Karnataka. The overall between-laboratory agreement was for 61·6 % for IQA and 64·8 % for EQA. The mean difference between iodine content estimation of field laboratories and the reference laboratory was 0·3 ppm (sd 8·2 ppm) for IQA and -0·3 ppm (sd 3·5 ppm) for EQA. CONCLUSIONS: Our study successfully documents implementation of a laboratory iodization quality assurance protocol in laboratories supporting small-scale salt production facilities in India.
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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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".