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Fatores preditivos de morbidade nas ressecções pancreáticas esquerdas

2012· article· pt· W2011679201 on OpenAlexaff
Fábio Athayde Veloso Madureira, Philippe Grès, Rodrigo Rodrigues Vasques, H Levard, Bruto Randone, Brice Gayet

Bibliographic record

VenueRevista do Colégio Brasileiro de Cirurgiões · 2012
Typearticle
Languagept
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsCollège Montmorency
Fundersnot available
KeywordsMedicineGynecology

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the postoperative morbidity of distal pancreatic resections and to investigate its predictive factors. METHODS: The study was conducted retrospectively from a prospectively database maintained. From 1994 to 2008, 100 consecutive patients underwent left pancreatic resections. The primary variable of interest was postoperative morbidity, and various other characteristics of the population were simultaneously recorded. Later, for the analysis of predictors of postoperative morbidity, the subgroup of patients who underwent distal pancreatectomy with spleen preservation (n = 65) was separately analyzed with regards to the different techniques of section of the pancreatic parenchyma, as well as to other possible predictors of postoperative morbidity. RESULTS: Considering all left pancreatic resections performed, the occurrence of overall, relevant and serious complications was 55%, 42% and 20%, respectively. The factors predictive of postoperative morbidity after distal pancreatectomy with spleen preservation were the technique employed for section of the pancreatic parenchyma, age, body mass index and the performance of concomitant abdominal operations. CONCLUSION: The morbidity associated with pancreatic resections to the left of the superior mesenteric vessels was high. According to the stratification adopted based on the severity of complications, some predictive factors have been identified. Future studies with larger cohorts of patients are needed to confirm these results.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.042
GPT teacher head0.358
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2012
Admission routes1
Has abstractyes

Explore more

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