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Record W159590626 · doi:10.1177/070674370805300702

Major Depression Prevalence is Very High, but the Syndrome is a Poor Proxy for Community Populations' Clinical Treatment Needs

2008· review· en· W159590626 on OpenAlexaffvenue
Scott B. Patten

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2008
Typereview
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEpidemiologyMedicineProxy (statistics)MEDLINEPsychiatryPsycINFOClinical psychologyPsychologyPathology

Abstract

fetched live from OpenAlex

Objectives: Some basic questions about the epidemiology of major depression (MD) remain open to debate and interpretation. Prevalence is a case in point. There have been claims that prevalence has been both over and underestimated. This review is an attempt to reconcile this apparent contradiction. Method: A literature search was carried out using MEDLINE. Articles were screened for relevance in 2 stages and bibliographies were examined to identify additional relevant publications. Results: The claim that prevalence has been overestimated appears to hinge on a concern that current diagnostic criteria fail to adequately differentiate between pathological and nonpathological mood disturbances. These arguments pertain to the validity of diagnostic criteria rather than to the prevalence of the syndrome that the criteria define. Conversely, the claim that prevalence has been underestimated is based on studies providing evidence of recall bias. If DSM-IV criteria are accepted as a diagnostic definition, MD prevalence is considerably higher than usually cited figures. However, the same literature indicates that the spectrum of severity is much broader than is usually acknowledged. The DSM-IV criteria appear to be a poor proxy for treatment need in community populations. Conclusions: Increasing evidence suggests that MD is very common but also that DSM-IV and ICD-10 definitions capture such a broad spectrum of morbidity that they should not be regarded as de facto indicators of need, at least not in community populations. Résumé: La prévalence de la dépression majeure est très élevée, mais le syndrome est un piètre indicateur des besoins de traitement clinique des populations communautaires Objectifs: Des questions fondamentales sur l'épidémiologie de la dépression majeure (DM) demeurent ouvertes au débat et à l'interprétation. La prévalence est un exemple typique. On a allégué que la prévalence était à la fois surestimée et sous-estimée. Cette étude tend à concilier cette contradiction apparente. Méthode: Une recherche de la documentation a été menée à l'aide de MEDLINE. Les articles ont été repérés pour leur pertinence en 2 phases, et les bibliographies ont été examinées afin d'identifier d'autres publications utiles. Résultats: L'allégation que la prévalence est surestimée semble reposer sur une crainte que les critères diagnostiques actuels ne différentient pas adéquatement entre les perturbations de l'humeur pathologiques et non pathologiques. Ces arguments relèvent de la validité des critères diagnostiques plutôt que de la prévalence du syndrome que les critères définissent. Réciproquement, l'allégation que la prévalence est sous-estimée se base sur des études offrant des données probantes d'un biais de rappel. Si les critères du DSM-IV sont acceptés en tant que définition diagnostique, la prévalence de la DM est considérablement plus élevée que les chiffres habituellement cités. Cependant, la même documentation indique que le spectre de gravité est beaucoup plus large que ce qui est habituellement reconnu. Les critères du DSM-IV semblent être un piètre indicateur des besoins de traitement dans les populations des collectivités. Conclusions: Un nombre croissant de données probantes suggèrent que la DM est très répandue mais aussi, que les définitions du DSM-IV et de la CIM-10 englobent un spectre tellement large de morbidité qu'on ne devrait pas les considérer comme des indicateurs de fait des besoins, du moins pas dans les populations des collectivités.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.648
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.114
GPT teacher head0.385
Teacher spread0.271 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations53
Published2008
Admission routes2
Has abstractyes

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