Bibliographic record
Abstract
The Canadian Society of Internal Medicine (CSIM) held an annual session to present the “Top 5 papers” influencing the practice of general internists. We reviewed major journal publications from January 2020 to November 2020 to come up with approximately 10 articles we considered practice changing trials for general internists. Out of those papers, we decided to present the five we considered were most relevant by addressing frequent pathologies seen in practice, were methodologically well conducted, and had the potential to sustainably modify practice guidelines. The references to the papers that were not retained are presented in the bibliography section for the reader’s interest. This article aims to present those top five papers of 2020, and to review their strengths and limitations. These articles were also discussed at the CSIM Virtual Educational Activity on October 15, 2020 and in the BaladoCritique podcast. RésuméLa Société canadienne de médecine interne (SCMI) a tenu une séance annuelle pour présenter les « cinq meil-leurs articles » qui influencent la pratique des internistes généralistes. Nous avons examiné les publications des principales revues publiées entre janvier 2020 et novembre 2020 pour en arriver à proposer environ dix articles que nous avons considérés comme des essais pouvant influencer la pratique des internistes généralistes. Parmi ces articles, nous avons décidé d’en présenter cinq qui, selon nous, sont les plus pertinents en abordant des pathologies fréquemment observées dans la pratique, sont bien menés sur le plan de la méthodologie et ont le potentiel de modifier de façon durable les directives de pratique. Les références des articles qui n’ont pas été retenus figurent dans la bibliographie pour l’intérêt du lecteur. Cet article vise à présenter les cinq meilleurs articles de 2020 et à examiner leurs forces et leurs limites. Ces articles ont également fait l’objet de discussions lors de l’activité éducative virtuelle de la SCMI qui s’est tenue le 15 octobre 2020 et dans un épisode du BaladoCritique.
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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 | 0.053 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.027 | 0.017 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.026 | 0.012 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.043 | 0.013 |
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".