Bibliographic record
Abstract
It is hard to believe but Clinical and Investigative Medicine (CIM), the official journal of Canadian Society for Clinical Investigation (CSCI), will soon celebrate its 40th birthday! Over these past four decades, CIM has been the premier journal for Canadian clinician scientists; publishing over 1,000 articles on breakthroughs and major advances from Canada and around the world. We are listed on Medline, PubMed and the Library of Science. We have been, and will continue to be, an independent journal. To celebrate this auspicious occasion, we have plans to become an even bigger showpiece for national and international clinical advances. We want to connect more closely with Canadian clinician scientists and trainees and we particularly want to encourage more Canadian publications. Changes will soon be coming to CIM with several new features: Newsletter with announcements and news on activities of interest to clinician scientists and trainees; Focused Reviews on specific areas of research; Reflections on work and life experiences of trainees and senior clinician scientists; Methods Papers describing novel methods anticipated to be useful for others; and Guidelines or Recommendations on clinical care that are endorsed by a Canadian Medical or Surgical Society. Starting in 2018, we will be publishing on a quarterly basis. This will help to ensure we will focus on important breakthroughs and commentaries. However, we are also planning a special edition in the autumn to commemorate the 40th birthday. Stay tuned! Of course CIM will continue to publish original papers on discoveries in pathophysiology, prevention, management, treatment and outcome of clinical problems confronting clinicians in Canada and around the world. Please join us as we embark on these changes and a new era for CIM, Robert Bortolussi Clinical and Investigative Medicine (CIM) Editor in Chief.
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 | 0.012 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.041 | 0.038 |
| Insufficient payload (model declined to judge) | 0.037 | 0.040 |
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".