Methodology of Systematic Reviews and Recommendations
Bibliographic record
Abstract
Although research in the field of spinal cord injury (SCI) is a relatively new endeavor, a remarkable number of papers focused on this subspecialty have been published in a broad variety of journals over the last two decades. A multidisciplinary group of experts, including clinical epidemiologists, neurosurgical and orthopedic spine surgeons, basic scientists, rehabilitation specialists, intensivists, and allied health professionals (nursing and physical therapy) was assembled through the Spinal Cord Injury Solutions Network to summarize the existing literature focusing on 12 key topics related to acute traumatic SCI, which have not been recently reviewed. The objective was to develop evidence-based recommendations to help translate current science into clinical practice and to identify new directions for research. For each topic one to three specific questions were formulated by consensus through the expert panel. A systematic review of the literature was performed to determine the current evidence for the specific questions. A primary literature search was performed using MEDLINE, CINAHL, EMBASE, and Cochrane databases. A secondary search strategy incorporated additional articles referenced in significant publications (i.e., meta-analysis, systematic and nonsystematic review articles). Two reviewers independently reviewed the titles and abstracts yielded by this comprehensive search and subsequently selected articles based on the predetermined inclusion and inclusion criteria. Data were extracted for population into evidentiary tables. Selected articles were rated for level of evidence and methodological quality, information that was also included in evidentiary tables. Disagreements were resolved by a third reviewer or consensus-based discussion. Based on the evidence compiled, answers to the targeted questions were formulated and recommendations generated by consensus-based discussion and anonymized voting using Delphi methodology. A level of consensus of 80% or higher was considered to represent strong agreement.
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 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".