Evaluating simple diagnostic tests for subclinical mastitis among Ghanaian lactating women
Bibliographic record
Abstract
Subclinical mastitis (SCM) has been associated with poor infant growth and HIV transmission. Diagnosis of SCM is expensive and not routine. We compared the sensitivity and specificity of simple, inexpensive tests, traditionally used with dairy cattle, with sodium potassium ratio (Na/K) as the standard, among 117 lactating Ghanaian women 3–4 mo postpartum. Milk samples were analyzed using Na/K, somatic cell count (SCC), California Mastitis Test (CMT) with and without Trace score, and electrical conductivity (ELEC). SCM prevalence using Na/K was 31.0%. Na/K was correlated with ELEC (r=0.586, p<0.01), SCC (r=0.372, p<0.01) and CMT (r=0.431, p<0.01). There were significant correlations between SCC and CMT (r=0.679, p<0.01); SCC and ELEC (r=0.497, p<0.01); and CMT and ELEC (r=0.493, p<0.01). There was significant agreement in SCM diagnosis between Na/K and SCC (K =0.271, p<0.01); Na/K and ELEC (K =0.291, p<0.01); and Na/K and CMT (K =0.126, p<0.05). The sensitivity and specificity, respectively, were: CMT (52.2%; 58.8%), CMT without Trace score (17.9%, 92.5%), ELEC (40.6%, 86.2%), SCC (42.9%, 82.8%). Despite low sensitivity, the high specificity of these simple tests could prove useful in clinical settings where there are limited resources for supporting lactation. Funded by SPRIGS/ISU and NIH #HD43620.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".