Bibliographic record
Abstract
Context and setting There are many ways to describe the side-effects of beta-blocker therapy, but for the past few years our students have been able to recall the liturgy of unwanted effects by remembering the lyrics to the ‘Beta-Blocker Blues’: ‘Got the blues; got them beta-blocker blues, Cold feet, cold toes, cold fingers, ice-cold beta-blocker blues.’ Why the idea was necessary The enthusiasm of our students for this unique form of teaching has been the impetus to create more ‘pharmacology blues’. The repertoire now includes ditties describing the side-effects of glucocorticoids (‘Moon-Face Blues’), problems of polypharmacy (‘Potentol Rag’) and an explanation of the Henderson−Hasselbach equation for absorption of weak acids and bases (‘Messrs H & H’). The song entitled ‘Zero Tolerance for You’ describes a series of pharmacological definitions defined in terms of a (decaying) romantic relationship. In addition to detailing drug side-effects, the songs can highlight other problems associated with drug use. For example, the benefits and drawbacks of prophylactic therapy are illustrated in a song about cholesterol-reducing strategies (‘Down to Five’). The problem of using 1 drug to treat the side-effects of another is dealt with in the song ‘Mr Miller’s Magic Panacea Pills'. The exotic sources for many widely used cytostatics are described in ‘Cancer Chemotherapy’. In this way, students can be made more aware of aspects of drug therapy that might not otherwise be covered in the lecture course: ‘My baby’s gone and left me – she says our love is dead, But bradycardia all day long stops fireworks in our bed.' What was done Last year, we were able to record the songs digitally in a professional studio. They can now be accessed by the global community of students and faculty staff from our web page (http://www.oppi.uku.fi/opk/video/ujbb/). The songs are available in mp3 format and the lyrics can be downloaded separately in either pdf or Powerpoint®. Evaluation of results and impact We have found that students can and do learn basic pharmacological facts simply by listening to the songs at home (pre-CD scores 30 ± 3%; post-CD 72 ± 4%), but we frequently incorporate the songs into appropriate lectures (e.g. ‘Alzheimer Blues’ in a lecture on antidementia drugs). In addition, they fit very well into revision quiz sessions: interspersing the songs between the rounds of a quiz makes it entertaining; more like a TV show rather than a dull revision session. In the months that the songs have been online, we have received positive responses from pharmacology teachers all over the world. These songs can be considered as ‘light edutainment’ but we are gratified that students' examination answers on the side-effects of beta-blocking drugs have improved since the advent of the ‘Beta-blocker Blues’: ‘These pills don’t stop my heartache – beta-blockers go to hell.'
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.017 |
| Insufficient payload (model declined to judge) | 0.031 | 0.022 |
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".