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Adolescent Depression Screening: Not So Fast

2019· article· en· W2951883654 on OpenAlexaboutno aff
Edmund C. Levin

Bibliographic record

VenueAdolescent Psychiatry · 2019
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
Fundersnot available
KeywordsDepression (economics)Value (mathematics)Conflict of interestPsychiatryMental healthMedicinePharmaceutical industryTask forcePublic relationsPsychologyPolitical scienceLawPublic administration

Abstract

fetched live from OpenAlex

Background: Screening adolescents for depression has recently been advocated by two major national organizations. However, this practice is not without controversy. Objective: To review diagnostic, clinical, and conflict of interest issues associated with the calls for routine depression screening in adolescents. Method: The evaluation of depression screening by the US Preventive Services Task Force is compared and contrasted with those of comparable agencies in the UK and Canada, and articles arguing for and against screening are reviewed. Internal pharmaceutical industry documents declassified through litigation are examined for conflicts of interest. A case is presented that illustrates the substantial diagnostic limitations of self-administered mental health screening tools. Discussion: The value of screening adolescents for psychiatric illness is questionable, as is the validity of the screening tools that have been developed for this purpose. Furthermore, many of those advocating depression screening are key opinion leaders, who are in effect acting as third-party advocates for the pharmaceutical industry. The evidence suggests that a commitment to marketing rather than to science is behind their recommendations, although their conflicts of interest are hidden in what seem to be impartial third-party recommendations.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.007

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 head0.271
Teacher spread0.255 · 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; both teacher heads agree on what is shown here.

Study designObservational
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

Citations4
Published2019
Admission routes1
Has abstractyes

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