Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
It is estimated that 2% of the population from industrialized countries live with lifelong disabilities resulting from traumatic brain injury (TBI) and roughly one in four adults are unable to return to work 1 year after injury because of physical or mental disabilities. TBI is a significant public health issue that causes substantial physical and economical repercussions for the individual and society. Electronic databases (PubMed, Web of Science, Google Scholar) were searched with the keywords traumatic brain injury, TBI, genes and TBI, TBI outcome, head injury. Human studies on non-penetrating traumatic brain injuries reported in English were included. To provide health care workers with the basic information for clinical management we summarize and compare the data on post-TBI outcome with regard to the impact of genetic variation: apolipoprotein E (APOE), brain-derived neurotrophic factor (BDNF), calcium channel, voltage dependent P/Q type, catechol-O-methyltransferase (COMT), dopamine receptor D2 and ankyrin repeat and kinase domain containing 1 (DRD2 and ANKK1), interleukin-1 (IL-1), interleukin-6 (IL-6), kidney and brain expressed protein (KIBRA), neurofilament, heavy polypeptide (NEFH), endothelial nitric oxide synthase 3 (NOS3), poly (ADP-ribose) polymerase-1 (PARP-1), protein phosphatase 3, catalytic subunit, gamma isozyme (PPP3CC), the serotonin transporter (5-HTT) gene solute carrier family 6 member (SLC6A4) and tumor protein 53 (TP53). It is evident that contradicting results are attributable to the heterogeneity of studies, thus further researches are warranted to effectively assess a relation between genetic traits and clinical outcome following traumatic injuries.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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 it