Tigeciclina versus vancomycin más aztreonam en el tratamiento de infecciones complicadas de piel y tejidos blandos: Experiencia en Latinoamérica
Bibliographic record
Abstract
Introduccion: El tratamiento de infecciones complicadas de piel y tejidos blandos (ICPTB) puede representar un desafio. Se comparo la eficacia de tigeciclina versus vancomicina/aztreonam en pacientes con ICPTB en un estudio multicentrico; este articulo se refiere a la experiencia en Latinoamerica (LA). Metodo: Se asignaron, en forma randomizada, los pacientes a dos grupos de tratamiento: tigeciclina o vancomicina/aztreonam. La meta a evaluar (outcome) primaria fue la curacion clinica, denominada test de curacion (TC). Se establecieron, ademas, metas secundarias y la evaluacion de seguridad del farmaco. Resultados: Un subtotal de 167 pacientes procedentes de LA, de un estudio multinacional que incluyo 573 pacientes, recibieron ≥ 1 dosis del farmaco en estudio. Al TC, los porcentajes de curacion fueron similares entre tigeciclina y vanco-micina/aztreonam en los pacientes clinicamente evaluables). La no inferioridad de tigeciclina no pudo ser demostrada (tamano de muestra insuficiente). Los pacientes tratados con tigeciclina tuvieron mayor incidencia de nauseas, vomitos y anorexia; los pacientes que recibieron vancomicina/aztreonam tuvieron mayor incidencia de prurito y rash. Conclusiones: Los resultados de eficacia en LA fueron consistentes con el estudio multinacional sugiriendo que tigeciclina no es inferior a vancomicina/aztreonam en el tratamiento de pacientes con ICPTB.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".