Simplified methods of determining treatment retention in Malawi: ART cohort reports vs. pharmacy stock cards
Bibliographic record
Abstract
SETTING: Rapid scale-up of antiretroviral therapy (ART) has challenged the health system in Malawi to monitor large numbers of patients effectively. OBJECTIVE: To compare two methods of determining retention on treatment: quarterly ART clinic data aggregation vs. pharmacy stock cards. DESIGN: Between October 2010 and March 2011, data on ART outcomes were extracted from monitoring tools at five facilities. Pharmacy data on ART consumption were extracted. Workload for each method was observed and timed. We used intraclass correlation and Bland-Altman plots to compare the agreeability of both methods to determine treatment retention. RESULTS: There is wide variability between ART clinic cohort data and pharmacy data to determine treatment retention due to divergence in data at sites with large numbers of patients. However, there is a non-significant trend towards agreeability between the two methods (intraclass correlation coefficient > 0.9; P > 0.05). Pharmacy stock card monitoring is more time-efficient than quarterly ART data aggregation (81 min vs. 573 min). CONCLUSION: In low-resource settings, pharmacy records could be used to improve drug forecasting and estimate ART retention in a more time-efficient manner than quarterly data aggregation; however, a necessary precondition would be capacity building around pharmacy data management, particularly for large-sized cohorts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".