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Record W4251254053 · doi:10.1177/070674371005501012

Disease versus Dimension in Diagnosis

2010· article· en· W4251254053 on OpenAlexvenueno aff
H. M. van Praag

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2010
Typearticle
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsnot available
Fundersnot available
KeywordsDimension (graph theory)PsychologyMedicineMathematicsCombinatorics

Abstract

fetched live from OpenAlex

Praag debated the wisdom of moving toward a dimensional system in psychiatric diagnosis, with Dr Shorter arguing that the existence of discrete brain diseases is not compatible with a dimensional system. 1 Dr Shorter cites melancholia, catatonia, attention-deficit hyperactivity disorder, and panic as examples, stating that each has a distinctive response to treatment and laboratory tests which substantiate the diagnoses.Unfortunately, space does not permit an examination of each of the examples he cites, so I will focus on several facets of melancholia to illustrate the errors in his argument.Dr Shorter states that melancholia is "the most solid disease" 1, p 59 found in psychiatry, noting the response to the dexamethasone suppression test (DST), and a distinctive response to tricyclic antidepressants and electroconvulsive therapy (ECT).Yet in 1989, Zimmerman and Spitzer 2 cited 12 studies that showed no evidence of a preferential response to somatic therapies in melancholia.Peselow et al 3 found similar results in 1992.The DST has fared no better, with a meta-analysis in 1997 4 finding no differences in melancholia, compared with nonmelancholic depression.More recently, other authors 5 concluded that the DST is probably not suitable as a biologic marker for depressive subtypes.Clearly, there are major problems with the DST in terms of sensitivity and specificity that Dr Shorter ignores.Moving beyond the arguments posed by Dr Shorter and Dr van Praag, there is another development that seems to pose a greater threat to the paradigm of disease and drug specificity.I refer to the growing approval of multiple drugs for the treatment of the same disorder, although the drugs have little in common with regard to their alleged mechanisms of action.For example, mania can be treated with antipsychotics, lithium, divalproex, carbamazepine, ECT, and lamotrigene.Major depression can be treated with ECT, antidepressants, several atypical antipsychotics, vagus nerve stimulation, cognitive-behavioural therapy, and transcranial magnetic stimulation.On the other hand, sertraline can be used in the treatment of major depression, posttraumatic stress disorder, panic, generalized anxiety disorder, obsessive-compulsive disorder, and premenstrual dysphoria.These developments in psychopharmacology are interesting from any number of perspectives, but most importantly seem to argue strongly against the specificity of drug or disease.I submit that from the clinician's viewpoint, we are already using a dimensional approach, whether we acknowledge it or not.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.041
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0010.008
Scholarly communication0.0040.005
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0130.001

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.

Opus teacher head0.017
GPT teacher head0.288
Teacher spread0.270 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations0
Published2010
Admission routes1
Has abstractno

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