A prática clínica baseada em evidências: parte II - buscando as evidências em fontes de informação
Bibliographic record
Abstract
The inadequacy of most of traditional sources for medical information, like textbook and review article, do not sustained the clinical decision based on the best evidence current available, exposing the patient to a unnecessary risk. Although not integrated around clinical problem areas in the convenient way of textbooks, current best evidence from specific studies of clinical problems can be found in an increasing number of Internet and electronic databases. The sources that have already undergone rigorous critical appraisal are classified as secondary information sources, others that provide access to original article or abstract, as primary information source, where the quality assessment of the article rely on the clinician oneself . The most useful primary information source are SciELO, the online collection of Brazilian scientific journals, and Medline, the most comprehensive database of the USA National Library of Medicine, where the search may start with use of keywords, that were obtained at the structured answer construction (P.I.C.O.), with the addition of boolean operators "AND", "OR", "NOT". Between the secondary information sources, some of them provide critically appraised articles, like ACP Journal Club, Evidence Based Medicine and InfoPOEMs, others provide evidences organized as online texts, such as "Clinical Evidence" and "UpToDate", and finally, Cochrane Library are composed by systematic reviews of randomized controlled trials. To get studies that could answer the clinical question is part of a mindful practice, that is, becoming quicker and quicker and dynamic with the use of PDAs, Palmtops and Notebooks.
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 | 0.106 | 0.195 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.019 | 0.013 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".