Bibliographic record
Abstract
Issu d’une initiative du Dr Robert Bachand de l’Universite de Montreal, le Precis d’anesthesie et de reanimation en est maintenant a sa 5e edition. Cette nouvelle version numerique comprend 40 chapitres couvrant les bases de l’anesthesie generale, de l’anesthesie locoregionale, de la reanimation et, bien sur, de l’algologie tant adulte que pediatrique. En plus d’etre abondamment illustree, la 5e edition inclut quelques courtes videos qui devraient faciliter l’apprentissage de certains sujets. Tous les chapitres ont comme premier auteur un anesthesiologiste expert dans son domaine choisi avec soin par moi-meme et mes deux principaux collaborateurs, les professeurs Rene Martin, de l’Universite de Sherbrooke, et Benoit Plaud, du Groupe Hospitalier et Universitaire Albert Chenevier – Henri Mondor de Paris. Le Precis d’anesthesie et de reanimation s’adresse aux etudiants en medecine, aux inhalotherapeutes, aux infirmiers anesthesistes ainsi qu’aux residents en anesthesiologie de premier niveau. Il se veut une introduction a la specialite. Nous avons donc essaye de l’ecrire dans un langage accessible a tous les niveaux.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.175 | 0.073 |
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".