Response to the letter “Actigraphic evaluation for patients with attention deficit/hyperactivity disorder”
Bibliographic record
Abstract
Dear Dr. Faraone, We would like to kindly respond to the letter submitted by Tomoyuki Kawada entitled “Actigraphic evaluation for patients with attention deficit/hyperactivity disorder.” This letter raised some issues claimed by the author regarding the use of the automatic device actiwatch in our study “Refining psychiatric phenotypes for response to treatment: contribution of LPHN3 in ADHD” published in your journal. Actiwatch device was used in our work to monitor the children's motor activity, as a potentially quantitative phenotype pertinent for response to methylphenidate in ADHD children. As the author mentioned, we did not use the standard procedure (sleep/wake judgment algorithm) available in the software named Actiware®. This sleep wake algorithm was not used simply because we did not include any data on sleep in our study. However, the relevant measure for our specific research interest was the original activity count recorded. The issue raised by the author is that activity from different actiwatch does not always agree with each other. Since different machines have been used on different children, the author claims that a calibration bias was present in our data. Although this is true that inter-machine variation can have an impact on the absolute value of activity, this effect has been implicitly taken into account when we computed the treatment effect measure for each subject, which was used as our main outcome in our analysis. The treatment effect was measured by subtracting the activity count obtained during the placebo week from the activity count from the active treatment week. Since the same actiwatch was used in both weeks for each child, the machine calibration effect is removed from our main outcome. As a result, we believe that the validation study proposed by the author is not relevant for our study. We hope that this answer meets your expectation, Sincerely yours, Aurelie Labbe, PhD, Assistant professor, McGill University, Department of Epidemiology and Biostatistics
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 | 0.002 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".