10.1016/s0186-0216(09)89050-2
Bibliographic record
Abstract
PlasticandHandSurgicalAssociatesesunaconsultaprivadadocenteque realizaentre3.500y 4.500operacionesquirurgicascadaano. Aproximadamenteel 95%de los pacientesotorgana nuestra consulta la maxima calificacion posible en las encuestas. Estoy convencido de que unode los factores fundamentalespara satisfacer o superar las expectativas quirurgicas de un paciente reside en el uso experto y liberal de los anestesicos locales por nuestros profesionales. De hecho, yo diria que, aparte de la limpieza con alcohol, los anestesicos locales representan la medicacion mas utilizada en nuestra consulta. Conforme va cambiando la economia de la medicina, a los cirujanos se nos pide que realicemos intervenciones mas cuantiosas y extensas de forma ambulatoria. De hecho, nosotros efectuamosoperaciones ambulatoriasque,hacemuypoco tiempo, solohubieramos planteado en un hospital. Ademas, hoy, intervenciones que antes se realizaban con anestesia general se efectuan bajo anestesia local. Un buen conocimiento de los anestesicos locales ayudaal cirujanoa satisfacer estademanda ya afianzar la seguridad, la experiencia y el confort del paciente. Aunque el eje de este numero de las Clinicas sea la cirugia menor, cualquier exposicion relevante sobre los anestesicos locales ha de irmas alla de una jeringa de 3 ml y de una pequena cantidad de lidocaina.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.003 |
| Insufficient payload (model declined to judge) | 0.986 | 0.987 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".