Bibliographic record
Abstract
The good old hockey game Y our recent report on the cardio- vascular effects of recreational hockey 1 touched a nerve.Three years ago, one of the guys in our league had a myocardial infarction while playing.It scared us all, and a few weeks later I went onto the ice -not one of us stopped playing -wearing a heart-rate monitor under my gear.I was able to monitor my heart rate continuously, and what I found closely paralleled the findings in this paper.I was astounded to see that my heart rate, which is normally around 80 when resting, shot up to 188, which is well above my cardiovascular exercise range.Did this stop me from playing, or cause me to modify my on-ice activities?I am Canadian, eh, so of course not.But what it did do was reinforce my commitment to off-ice conditioning.Press coverage of the CMAJ study failed to reinforce one of its key points: that we can continue playing recreational hockey but we should be in the proper physical shape to do it wisely and safely.The message for me was that in our attempts to recapture the glory of our youth we may forget to apply to ourselves the wisdom and common sense that our profession expects us to use with our patients.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.070 | 0.033 |
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".