Yonder: Medically unexplained symptoms, dysmenorrhoea, mental health stigma, and YouTube
Bibliographic record
Abstract
MUS. ‘Medically unexplained symptoms’ can be an uncomfortable and challenging diagnostic label for doctors and patients alike. It represents some of the most technically difficult and yet rewarding aspects of being a GP, such as dealing with uncertainty and taking a holistic, person-centred approach. In a Qualitative Health Research study, Canadian researchers sought to explore the experiences of individuals who had fallen into the umbrella of this diagnostic label. 1 They identified three experiential stages: searching for a diagnosis, living with uncertain symptoms, and finally, acceptance of their condition. Importantly, in light of the current strain on general practice in the NHS, the emphasis placed on the importance of relationship-based care is highly relevant and the challenge to maintain continuity seems to be particularly important for this population. Dysmenorrhoea. This can be a debilitating condition for many women with the potential to significantly reduce quality of life as well as lead to absence from education and employment. Having identified an absence of patient-reported outcome measures in dysmenorrhoea, a group of researchers recently sought to develop a new measure that could be used in clinical trials. 2
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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".