Special Report Personal Reflections From a Front-Row Seat at the Greatest
Bibliographic record
Abstract
Part I of this discussion of my 65 years since graduatingfrom medical school covered my reasons for being in medicine at all, my explanation for the theatrical terms in the earlier para-scientific document, and finally the effect of World War II on my career.1 Part II will attempt to touch on the privileged life I led in the field of stroke prevention research. In its entirety it is designed as a story or tale about my 50 years in medical practice and does not follow the standard format of a scientific paper. Previously I alluded to the ongoing prejudicial custom of critical attempts to resist new ideas in scientific endeavor. I gave as examples the discordant behavior in Toronto that greeted the discovery of insulin and of less far-reaching consequence my own attempts to convince Canadian and American colleagues that there were 5 distinct causal mech-anisms leading to syringomyelia. Surgeons in Cleveland and Boston were adamant that there was but one. Postmortem studies and now routine MR studies prove them wrong.
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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.043 | 0.017 |
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".