Authors' Responses to Peer Review of “Machine Learning and Medication Adherence: Scoping Review”
Bibliographic record
Abstract
This paper [2] covers a very interesting area on the use of machine learning for assessment of medication adherence, yet in its current version, it does not add a lot to the field.It is a pity, as it seems that the authors performed their review well.However, the presentation of the results is not acceptable. Major Comments1.It creates a lot of confusion that the authors use "adherence" instead of "compliance."In fact, these two are equivalent terms, of which adherence is preferred and compliance is a bit old-fashioned.The authors need to define the major concept they use, and these two need to be carefully checked against available literature and the ABC taxonomy.Response: Definition explained under the updated Methods Eligibility Criteria section.This taxonomy defines medication adherence as "The process by which patients take their medications as prescribed, composed of initiation, implementation and discontinuation" [3]. 2. The Abstract provides no numeric data; even the number of identified publications is missing.Similarly, the conclusions of the Abstract are inconclusive.Response: This point is addressed mostly in the updated Abstract and the updated Discussion/Conclusions section.3. The authors mentioned previous reviews in this area, yet they did not make it clear what was different about their own work.What exactly was missing in the previous reviews that turned them toward this new exercise?Response: This is addressed under Introduction, paragraphs three and four.4. Publication selection for review: What were the criteria used to identify acceptable papers in the full-text review?What was the reason for screening a sample of 20 papers first?Response: Updated the eligibility criteria and selection of sources of evidence sections to address this issue.5. "Medication adherence activities" is not a term used in the literature to describe interventions aimed at assessment or modification of medication adherence.Please use another term that is used in the existing literature.Response: I have determined that the creation of a new term is not necessary to explain my ideas in this part of the paper.I have changed medication adherence activities to verbs related to medication adherence.In this way, I can explain my idea without introducing new terminology that is potentially confusing for the reader.The changes are located in the analysis of natural categories paragraph and throughout the manuscript.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.113 | 0.589 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.023 | 0.013 |
| Insufficient payload (model declined to judge) | 0.048 | 0.024 |
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".