Bibliographic record
Abstract
Objective: To identify which oxidative stress markers can influence early stroke prognosis. Methodology: This is a systematic review carried out in two databases, PubMed and Web of Science, from November to December 2018. Two blinded, independent researchers probed the databases and gleaned descriptors indexed on both sites. “Markers”, “oxidative stress” AND “stroke” were the terms singled out for this study. The inclusion criteria were: the articles needed to have been published in English from 2013 to 2018, as well as include descriptors either in the title or in the abstract, and involve clinical trials with samples composed of stroke survivors. The exclusion criteria were: research involving animal experimentation; duplicate publications; articles without a clear methodology; articles that chiefly addressed any disease other than stroke, and those that were not available in full. Results: This review identified TBARS, catalase, nitric oxide (NO), Thiois, C-reactive protein and SOD as the most recurrent oxidative regulation markers in stroke survivors. These findings may direct new research toward obtaining early prognoses, and therefore enable more accurate decision-making. thus minimizing the costs and time related to the patient rehabilitation process.
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 | 0.011 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".