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

Mind your state: Insights into antidepressant nonadherence

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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.584
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.002

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