Global Health Journal Club—Opening Editorial Applying Evidence-based Medicine in Resource-Limited Nations
Bibliographic record
Abstract
A traditional healer describes cases of children who are ‘normally attacked by spirits which cause uncoordinated movement of eyes and limbs. The spirits easily and quickly leave the child when urinated on or fumigated with elephant dung smoke’ [1]. Most practicing clinicians would immediately be skeptical of such treatments and would not need an evidence base to form such a judgment. But what about being faced with a premature infant with a common problem such as a patent ductus arteriosus and the dilemma of choosing between oral ibuprofen and intravenous indomethacin, in terms of safety and efficacy, affordability and availability? (See this month’s first edition of the Global Health Journal Club). Journal clubs are a well-recognized and well-accepted quality improvement strategy used by health practitioners to critique and keep up-to-date with relevant health literature. Journal clubs have been shown to improve clinical knowledge, knowledge of biostatistics, research design, reading habits and critical appraisal skills [2, 3]. Since William Osler’s report of ‘The Book and Journal Club’ at McGill University in 1875, the journal club has assumed many forms and has served many functions [3], although there is little information on the most effective way of conducting a journal club to maximize educational benefit [2]. In this journal, we propose a new form, a Global Health Journal Club. Our Global Health Journal Club will move away from the traditional weekly physical departmental meeting toward a peer-reviewed, virtual journal club structured on the steps of evidence-based medicine (EBM) [4, 5]: Step 1: Asking a question: EBM relies on an inquisitive mind. The questions that rise in our minds can be structured using the PICO structure (Patient, Intervention, Control, Outcomes) Step 2: Acquiring information/evidence: Using established databases (e.g. PubMed) to find previous studies or research conducted, both those that have been published and those that remain unpublished Step 3: Appraising the information/evidence: Using a set of criteria to evaluate the quality of the studies found during the search Step 4: Applying the evidence to your patient or population: Use the evidence that has been found and appraised to change and improve practice Step 5: Assessing your performance: Monitor what has been done and ensure that it is effective. Provide feedback for the progress of performance
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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 | 0.015 | 0.073 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.023 | 0.010 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.020 | 0.018 |
| Insufficient payload (model declined to judge) | 0.034 | 0.019 |
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".