Bibliographic record
Abstract
Our understanding of the prevalence of mental health disorders (MHDs) in society is in the midst of a paradigm shift: where MHDs were once considered rare within a population, studies through the last decade have converged to the conclusion that they are, in fact, near universal. Consequently, the demand for mental health treatment has resulted in the training of Primary-Care Physicians (PCPs) to identify, diagnose, and treat common MHDs. As generalists, PCPs require specialised point-of-care clinical resources to educate their patients and provide them with evidence-based treatment plans; UpToDate is one such resource. As a database of synthesized peer-reviewed medical information, written and approved by physician-experts from their review of contemporary peer-reviewed literature, this resource is considered a gold standard. Here, we examine an MHD-specific investigative case study on Generalized Anxiety Disorder where the synthesized UpToDate medical information was found to be in conflict with the original studies. In this era of unrelenting bombardment of digital data, the responsibility of assessing the truth of the information falls to the consumer. While a reliance on reputable information-sharing platforms facilitates both the access and assessment of truth, we discuss the risks of unintended errors, their propagation, and the potential impact at the point-of-care.
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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 | 0.245 | 0.561 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.036 | 0.051 |
| Open science | 0.013 | 0.041 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.037 | 0.016 |
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".