All you need to read in the other general journals
Bibliographic record
Abstract
N Engl J Med2010; doi:10.1056.NEJMa0905370 An international team of researchers used functional brain imaging to look for residual consciousness in unresponsive patients with severe brain injury. Five of the 54 patients tested seemed able to “think” on demand, producing brain images that matched those of healthy controls given the same instruction. Four of them were in a persistent vegetative state after traumatic brain injury. The fifth was in a minimally conscious state—a label given to patients with minimal and inconsistent signs of awareness. During magnetic resonance imaging (MRI), they were able to imagine a motor task (playing tennis) or a spatial task (navigating around their home or familiar streets) apparently at will, producing characteristic changes in the supplementary motor area of the brain or in the parahippocampal gyrus. One of the five patients also seemed able to answer simple yes or no questions, by imagining one or other of the images during scanning—tennis for yes, and navigation for no. His doctors were unable to establish any kind of consistent communication at the bedside, although they upgraded his clinical diagnosis to minimally conscious (from persistently vegetative) after the scans and further behavioural testing. The authors hope their technique will help evaluate people who are unresponsive after severe brain injuries and cut the risk of misdiagnosis. A linked comment (doi:10.1056/NEJMe0909667) hopes so too, but it cautions against giving false hope to relatives and friends. Cortical activation was rare, even in these handpicked patients. We still have little idea what kind of consciousness, if any, it signified. JAMA2010;303:423-9 [OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Chronic kidney disease is currently classified and staged using an estimate of the glomerular filtration rate (eGFR). Proteinuria matters too, according to a large observational study from Canada⇑. The authors had data on nearly one million adults, classified by the eGFR into four strata. Within each one, heavier … [1]: {openurl}?query=rft.jtitle%253DJAMA%26rft.stitle%253DJAMA%26rft.issn%253D0002-9955%26rft.aulast%253DHemmelgarn%26rft.auinit1%253DB.%2BR.%26rft.volume%253D303%26rft.issue%253D5%26rft.spage%253D423%26rft.epage%253D429%26rft.atitle%253DRelation%2BBetween%2BKidney%2BFunction%252C%2BProteinuria%252C%2Band%2BAdverse%2BOutcomes%26rft_id%253Dinfo%253Adoi%252F10.1001%252Fjama.2010.39%26rft_id%253Dinfo%253Apmid%252F20124537%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1001/jama.2010.39&link_type=DOI [3]: /lookup/external-ref?access_num=20124537&link_type=MED&atom=%2Fbmj%2F340%2Fbmj.c754.atom [4]: /lookup/external-ref?access_num=000274143600017&link_type=ISI
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.723 | 0.707 |
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".