Response to Questionable assumptions mar modelling of Kenya home‐based testing campaigns ‐ a comment on “Optimal timing of <scp>HIV</scp> home‐based counselling and testing rounds in Western Kenya” (Olney et al. 2018)
Bibliographic record
Abstract
We appreciate the letter in response to our article “Optimal timing of HIV home-based counselling and testing rounds in Western Kenya 1.” The authors appraise the assumptions made by the model and question whether the modelled HBCT programme in western Kenya could be improved 2. As the authors are aware, high prevalence settings often require active outreach to identify a meaningful proportion of the infected population 3. However, timely linkage to care following diagnosis remains a challenge 4. Several studies are looking at innovative means of addressing this, including the use of peer navigators 5, mHealth initiatives 6, and same-day ART start 7. Meanwhile, a different - but still valid - question concerns how best to use an existing programme, in this case through repeating it to test more people. The model is based on real data from AMPATH which arguably makes it conclusions better suited to policy than arguments based on hypothetical extrapolations. It was necessary to make assumptions, and these were not with the intention of being cautiously conservative about the impact of the programme. Further model analyses could indeed compare the impact of different types of HBCT programme, as well as assessing ancillary benefits such as knowledge transfer to the community and NCD testing. The authors declare that they have no competing interests. JJO and TBH drafted the initial version of the letter. JWE, PB and JWH reviewed and provided revisions to the draft prior, before JJO circulated the finalized letter for approval from all authors prior to submission. The authors thank the editor and the authors of the originating letter for providing the opportunity to discuss this important topic.
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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 | 0.038 | 0.297 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.027 | 0.044 |
| Insufficient payload (model declined to judge) | 0.024 | 0.011 |
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".