Bibliographic record
Abstract
The British Medical Bulletin has another edition for the last quarter of 205. This set of reviews is, as usual, about developing ideas in medicine for trainee specialists and people who are wishing to get up-to-date in a specialty with which they are not familiar. Reviews that we believe to be of particular and general interest will appear on the Oxford University Press blog. The first review for the present edition is entitled ‘Potential compounds for the treatment of mitochondrial disease’ (page 5) by Rai, Russell, Lightowlers and Turnbull from Newcastle University, UK. They say that mitochondrial diseases are a group of heterogeneous disorders for which no curative therapy is currently available. Several drugs are currently being pursued as candidates to correct the underlying biochemistry that causes mitochondrial dysfunction. Several compounds under investigation display poor pharmacokinetic profiles or numerous off-target effects. Drug development teams should continue to screen existing and novel compound libraries for therapeutics that can enhance mitochondrial function. Therapies for mitochondrial disorders could hold potential cures for a myriad of other ailments associated with mitochondrial dysfunction such as neurodegenerative diseases.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.201 | 0.171 |
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".