Reply to Letter to the Editor: “A Complex Phenomenon: Medication Overuse Headache and Childhood Experiences”
Bibliographic record
Abstract
We thank the authors for their letter to the editor 1 regarding our study on adverse childhood experiences (ACEs) and medication overuse headache (MOH). 2 We were pleasantly surprised to receive a letter after such a length of time since our paper was first published in November 2022.We will respond to the several interesting points raised in the letter.With respect to the literature review performed by the authors, we observe with surprise the inclusion of two papers by the authors themselves, neither of which relate in any clear way to the subject matter claimed.For example, the authors cite their own study using a scale assessing headache-related disability in pediatric cluster headache in support of their suggestions about smoking, working status and alcohol use contributing to migraine-related disability (reference 6 in the submitted letter).We would like to suggest that migraine and cluster headache are distinct disorders, as defined by ICHD-3, 3 and a study assessing disability using a cluster headache scale in a pediatric population cannot be suggested to apply to migraine-related disability in adults.The authors would have been better served to cite original literature to support their point.The authors also cite their own study about telehealth in pediatric migraine when making a statement about the best method for withdrawing some medications such as non-opioid analgesics (reference 4 in the submitted letter).We are again unsure of the relevance of their pediatric telehealth satisfaction study to this point, and in our detailed reading of their paper, neither medication overuse nor medication withdrawal appear to be mentioned at any point.We would also like to highlight that neither of the two large published randomized controlled trials on the subject of the best strategy for managing MOH suggest that abrupt withdrawal is required, and we argue that a more patientcentred approach would allow for tailoring of treatment plans to the individual patient.4,5 We clearly outlined that our patients received multimodal treatment, which commonly included bridge therapy with a long-acting nonsteroidal anti-inflammatory (NSAID).This was an outpatient retrospective study and no specific withdrawal methodology was employed; in particular we do not perform opioid withdrawal therapy and no patients were encouraged to abruptly stop opioids, due to concerns for opioid withdrawal.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.024 | 0.023 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".