Bibliographic record
Abstract
The 2008 Canadian Paediatric Society Annual Meeting held in Victoria, British Columbia, was my first conference as a fully retired member. As I watched the younger delegates striding by, I thought to myself how confident they looked and reflected on how I must have seemed at the same stage of my career. I admit that my feelings included the sentiment, “if I only knew then what I know now….” I started my paediatric training at The Montreal Children's Hospital (Montreal, Quebec) in the summer of 1964. Those were wonderful days and, as the late Allan Ross was fond of saying to us, “they would be the best years in your careers”. We saw lots of ‘pathology’, had a free hand in case management and had the benefit of exposure to wonderful mentors. Those were the days of measles epidemics, frequent meningitis outbreaks and adolescents dying from cystic fibrosis – so different from the typical caseloads in today's teaching hospitals. There were a multitude of clinics but there was a dearth of nonemergent ambulatory care experience. There were no opportunities for international or remote area electives. Health promotion was not even in our lexicon. In addition, we had no pagers, cell phones, laptops, wireless Internet, intravenous insertion teams, computed tomography or magnetic resonance imaging. Thanks to advances in investigation and treatment, today's paediatric residency experience is much different and our understanding of diseases more sophisticated. However, there continues to be a heavy emphasis on infants and children, and much less exposure to contemporary adolescent medicine.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.014 | 0.021 |
| Insufficient payload (model declined to judge) | 0.083 | 0.038 |
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".