Modifiable factors associated with loss of donors in a human milk bank
Bibliographic record
Abstract
Objective To identify factors associated with the increased risk of loss of human milk donors (HM) to the milk bank (MB) of the Hospital General de Medellín (HGM) between 2014 and 2019. Methodology A total of 559 women who contacted the MB to be HM donors between 2014 and 2019 were evaluated according to their classification as contact eligible or ineligible to donate. A logistic regression model was used to identify the variables associated with the classification of a contact as ineligible. Results A total of 8.8% (n=49) of contacts were classified as ineligible. Ineligible contacts were 1.8 years older, with twice as many being exclusive donation method users. A higher percentage of ineligible contacts produced milk from preterm babies or colostrum. A higher percentage were classified as ineligible during the first years of the MB's operation, and a higher percentage had not undergone diagnostic tests for sexuallytransmitted infections in the last year. Additionally, 22.9% had been diagnosed with anemia during gestation (P<0.05). Contacting the MB between 2014-2016 (OR=3.08; P=0.004) and being from the exclusive donation method (OR=3.11; P=0.004) increased the risk of being classified as an ineligible contact. The absence of an HIV diagnostic test and a diagnosis of anemia during gestation were considered exclusion factors. Conclusion Modifiable factors increased the risk of a contact being classified as ineligible to donate human milk, identifying and treating them would allow increasing the number of HM donors to a MB.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".