Assessing and measuring cognitive function in major depressive disorder
Bibliographic record
Abstract
Cognitive dysfunction is a major component of major depressive disorder (MDD). No 'gold-standard' tool exists for the assessment of cognitive dysfunction for adults with MDD. The use of measurement-based care to improve treatment outcomes invites the need for a systematic screening, evaluation and measurement tool. The aim herein was to provide a succinct summary of literature documenting clinical implication of cognitive dysfunction in MDD, and a review of available screening, diagnostic and measurement tools for cognitive dysfunction in MDD is provided. We also take the opportunity to introduce a screening tool (ie, the THINC-it tool) targeted at addressing the unmet needs. We found that there are limitations to the current measurement scales; for example, many are not targeted for MDD and not all digitally available tests are free of charge. Furthermore, the spectrum of cognitive dysfunction in MDD is poorly represented by the existing tests and as such, there is a lack of sensitivity in the ability to screen a patient with MDD for a cognitive dysfunction. Recognising and addressing the limitations in the current screening techniques for cognitive dysfunction as well as being presented with the current tools available provides the ability to perform an educated cognitive screening for a patient with MDD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".