Bibliographic record
Abstract
rology is serious business.As John Trachtenberg, learned urologist and prostatectomist in Toronto, Canada, always said while operating when the situation called for immediate perspective: "Look down at the patient, he is the brave one here..…compared to him, we have it easy out here, always remember that."Nothing could be closer to the truth.We all deal with illness in a professional manner; in most instances, we do so in a humble, caring and respectable way.However, in many instances, the ability to release tension has been lost.People are too busy catching up and debriefing and there is a separation of work and life that is probably healthy, but perhaps at the cost of "taking work home" in more subtle, subconscious ways.To deal with this, we use humour.It has always been a part of medicine, particularly in surgery.Oftentimes, the humour was black.The healing powers of humour and humour ability to soften difficult situations are well-described.1,2 However, in recent years, humour has evaporated and been replaced with political correctness.3 So it was refreshing when a technician found a written message to the operating team from the patient on the operating table.The message, in the form of a poem, was carefully folded in the patient's underpants
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.035 | 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".