Communicating the Right Therapy for the Right Patient at the Right Time: Acute Therapy
Bibliographic record
Abstract
OBJECTIVE: Review of problems arising from communication difficulties in headache practice. METHODS: Literature review and assessment of practice experience. BACKGROUND: Advances in understanding of the pathophysiology of migraine and the availability of specific acute therapies have given migraine sufferers access to effective treatment and physicians a wide array of therapeutic alternatives. There remains uncertainty about the best drug group for any given patient and about which triptan to use when and in which formulation; about patient preference and satisfaction; about interpretations of pivotal trials and meta-analyses; and about the relevance of large group efficacy and safety data to the individual patient. The clinician may be daunted by the array of triptans with choices of dosage and multiple formulations and will likely learn how to use two or three of them at most, as in depression and hypertension. In the context of the wide array of choices and the complexities of assessing responses and patient preferences, this paper attempts to provide a framework for incorporating the evidence with clinical experience and for communicating these concepts effectively. BENEFITS, HARMS AND COSTS: None. RESULTS AND CONCLUSION: Even when an appropriate recommendation is determined, therapy may fail unless the doctor patient relationship permits open communication, time for questions and answers and time for instruction on how to use a given medication, and its probable effects. Translating evidence into patient-friendly language is a skill as necessary as that of making the clinical decision itself. Tools are available that can support this effort and aid in creating an environment of "partnership".
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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".