Searching for evidence-based medicine in wound care: an introduction.
Bibliographic record
Abstract
During the last 10 years, wound care knowledge and treatment options, as well as the amount of information in the literature pertaining to wound and patient treatment options, have expanded rapidly. As a result, clinicians need to be able to review the existing literature with knowledge of the steps involved in evidence-based medicine. By identifying search strategies to improve information retrieval, time can be saved, new knowledge can be obtained, and with an understanding of clinical experience and patient-centered concerns, the best evidence for decision making can be utilized. The information retrieved can be categorized according to the level of evidence and clinical practice guidelines (documents in which an expert panel has reviewed the evidence and interpreted it for patient care) can be measured by the Appraisal of Guidelines for Research and Evaluation (AGREE) Instrument. Learning how to find and interpret the literature not only enhances clinical decision making, but it also may inspire additional inquiries that will add to the existing evidence base.
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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.015 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".