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Record W2510460395 · doi:10.4102/safp.v58i4.4509

Mind your state: Insights into antidepressant nonadherence

2016· article· en· W2510460395 on OpenAlexaff
De Wet Wolmarans, S Brand

Bibliographic record

VenueSouth African Family Practice · 2016
Typearticle
Languageen
FieldMedicine
TopicTreatment of Major Depression
Canadian institutionsHealth Sciences North
Fundersnot available
KeywordsMajor depressive disorderMedicineAntidepressantPsychiatryDepression (economics)PopulationDiseaseDepressive symptomsInternal medicineAnxietyMood

Abstract

fetched live from OpenAlex

Major depressive disorder (MDD) is an insidious disease and affects up to 15% of the global population. Although MDD responds to a wide range of pharmacological treatment options, a number of factors, i.e. not adhering to treatment for at least 4–12 months, contribute to antidepressants not being highly effective. In an attempt to aid clinicians in improving the adherence rates among MDD patients, the current paper will divulge in more detail the possible explanations of why individuals with MDD find it difficult to adhere to prescribed regimens.

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)0.004
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.005
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.035
GPT teacher head0.302
Teacher spread0.267 · 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 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

Citations1
Published2016
Admission routes1
Has abstractyes

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