COMPORTAMENTO DOLOROSO E ANALGESIA PÓS-OPERATÓRIA EM PACIENTES SUBMETIDOS A TORACOTOMIAS
Bibliographic record
Abstract
Resumo: Objetivou-se investigar o comportamento da dor pos-operatoria citada pelos pacientes submetidos a toracotomia, segundo o analgesico prescrito. Metodologia: estudo descritivo com delienamento trasnsversal, realizado em dois hospitais de Natal, Brasil, conveniados com o SUS. A amostra foi composta por 40 pacientes, 24 do sexo masculino e 16 do sexo feminino, com idade media de 48,9 anos. Os instrumentos utilizados foram a ficha de avaliacao fisioterapeutica, a escala numerica de dor e questionario para dor McGill. Os procedimentos foram realizados atraves da aplicacao dos instrumentos. A escla numerica era mostrada ao paciente, sendo escolhido o numero zero a dez que melhor representava a sua dor. O McGill era era lido para o paciente que escolhia o descritor que melhor caracterizava a sua dor naquele momento. Resultados: Observou-se uma diversificacao de prescricoes analgesicas para os pacientes submetidos a toracotomias e tanto na escala numerica quanto no McGill houve predominância de dor moderada em 40% das respostas dos pacientes. Porem a dor apresentou uma tendencia a intensa em 22,5% na escala numerica e 35% no McGill. Conclusao: Constatou-se uma deficiencia quanto ao consenso e o uso de protocolos analgesicos no pos-operatorio de toracotomias, o que mostra que a dor continua sendo subtratada.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".