The diagnosis and treatment of chronic migraine: the case for daily scheduled opioid treatment in chronic headache
Bibliographic record
Abstract
As a clinical neurologist with special interest in headache, I read the recent article by Weatherall in your journal with interest [Weatherall, 2015]. However, I was astounded by the lack of any reference in the article to the therapeutic role of opiates in the management of headaches in those with acute or chronic migraine. This omission seems to have been based on the author’s emphasis on medication overuse headache (MOH) and his belief on a causal role played by opiates in the genesis of chronic daily headache (CDH) (he used those two terms interchangeably). Apart from the ongoing debate on the causative role of opiates and other analgesics in the development of MOH [Cady et al. 2011], the role of daily scheduled opioids in securing a headache-free existence to a significant portion of those suffering from disabling headaches is well documented in the literature [Levin, 2014; Robbins, 2014; Saper et al. 2004; Spierings 2003; Saper et al. 2010; Ziegler, 1994]. Similarly, opioids continue to be used as the last resort in treating pediatric and adult populations with headaches who visit emergency rooms across the United States and in Canada [Burch et al. 2015; Colman et al. 2004; Eapen et al. 2014]. In the words of Thomas Sydenham: ‘I cannot but break out in praise of the great God, the giver of all good things, who both granted to the human race, as a comfort in their afflictions, no medicine of the value of opium…. So necessary an instrument is opium in the hands of a skillful man that medicine would be a cripple without it’ [quoted in Ziegler, 1994].
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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.005 | 0.018 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.022 | 0.034 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".