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The dangerous charms of the unknown

2012· letter· en· W1533780857 on OpenAlexaboutno aff
Peter Reuter

Bibliographic record

VenueAddiction · 2012
Typeletter
Languageen
FieldMedicine
TopicPharmaceutical studies and practices
Canadian institutionsnot available
Fundersnot available
KeywordsScheduling (production processes)Drug controlLaw and economicsOperations researchComputer scienceLawBusinessPsychologyEconomicsOperations managementPolitical scienceEngineering

Abstract

fetched live from OpenAlex

Hughes & Winstock 1 move the ‘legal highs’ discussion forward by reviewing regulatory systems other than the traditional drug control apparatus under which these substances might be handled. Note, first, that the current system may be doing quite well by some important measures. Coulson & Caulkins 2 reviewed the 73 substances that had been scheduled by at least one of four countries (United States, United Kingdom, Australia, Canada). For each substance they asked whether there was evidence that the scheduling decision represented a Type I error; namely, a drug that should have been allowed on the market was scheduled. Using the existing literature they thought that no more than four of the 73 (and probably only one) represented an over-scheduling decision. Type II errors, where a dangerous substance was left unscheduled, obviously cannot be studied by looking at those that were scheduled. Examining the critical literature they found only three unscheduled substances that seemed to be in need of scheduling. As they note, this does not mean the system is perfect, but the claim of a major problem of over-scheduling or under-scheduling, at least for those four countries, is unpersuasive. The problems that Hughes & Winstock propose to solve, then, are narrower: (i) allowing new substances to properly label themselves as for human ingestion rather than claim deceptively to be something else, such as ‘bath salts’ or ‘plant food’, and (ii) reducing the time to banning the drug if necessary. It is hard to argue with the first of these, but the second may be less attractive than it appears. Prohibition is not a decision to be made lightly. Admittedly, the existing system has an element of farce. It requires review of what is almost always an extraordinarily thin scientific literature. The novelty of most of these drugs means that there has been little time to conduct research on the health consequences of use, let alone explore the population health consequences of the drug. However, the current system's requirement of elaborate collection and analysis of relevant information, with an emphasis on peer-reviewed material, has some distinct advantages compared to allowing a civil servant to make the decision without public scrutiny. David Nutt has critiqued a number of recent Advisory Council on Misuse of Drugs decisions in the United Kingdom for their excessive caution (e.g. 3); whether or not he is correct, it is only because of the current drawn-out procedures that the public is in a position to judge this matter. In addition, there surely must be some concern about the deceptiveness of using the medications system: recreational substances are being permitted under legislation that is aimed clearly at regulating goods that have therapeutic intent. The relevant clause in the European Union legislation is: ‘any substance or combination of substances which may be used in or administered to human beings either with a view to restoring, correcting or modifying physiological functions by exerting a pharmacological, immunological or metabolic action, or to making a medical diagnosis’. A recreational drug presumably meets the criterion of ‘modifying physiological functions’, but what does not? The answer may be to expand the relevant legislation so that fitting these new drugs under medications does not require intellectual contortions. Hughes & Winstock, following Birdwell, Chapman & Singleton 4 point us in an interesting direction. However, in examining the regulatory alternatives we should be sensitive to a potential bias in our thinking. We know very well the complexities and problems of the drug scheduling system. We need to become similarly intimate with the advantages and disadvantages of the alternatives before making a recommendation to shift systems. None.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.064
Threshold uncertainty score0.502

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.056
GPT teacher head0.335
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations8
Published2012
Admission routes1
Has abstractyes

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