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Record W2902625301 · doi:10.1055/s-0038-1676289

Maternal and Fetal Outcomes in Pregnancies affected by Bone and Soft Tissue Tumors

2018· article· en· W2902625301 on OpenAlexaff
Ernesto Antônio Figueiró-Filho, Hythem Al-Sum, Jacqueline Parrish, Jay S. Wunder, Cynthia Maxwell

Bibliographic record

VenueAmerican Journal of Perinatology Reports · 2018
Typearticle
Languageen
FieldMedicine
TopicCancer Risks and Factors
Canadian institutionsUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicineSoft tissueObstetricsFetusPregnancyRadiology

Abstract

fetched live from OpenAlex

Objective This study was aimed to describe perinatal outcome of a cohort of pregnant patients with bone and soft tissue tumors and to compare the current series with our group's previously reported experience. Methods Pregnant women diagnosed before and during pregnancy were identified, retrospectively, for the years 2004 to 2014. Relevant maternal and neonatal data were collected. Results Forty-eight patients were identified. Ten cases were diagnosed during pregnancy. Pelvis, abdomen, and extremities were the most common tumor locations. Osteosarcoma, liposarcoma, and Ewing's sarcoma were the most common histological types and comprise more than 50% of the cases. Metastases occurred in nine cases. Most of the cases (60%) were treated surgically during pregnancy and delivery occurred at term. Chemotherapy was delayed until after delivery. There were no perinatal or infant deaths. Patients presented with advanced maternal disease in 18% in previous report (1983–2003) versus 40% in present report (2004–2014). Metastases were present in 40% and maternal death rate was approximately 20% in both cohorts. Conclusion Pregnant women with bone and soft tissue tumors are candidates for standard surgical management during pregnancy. Other treatments, such as chemotherapy and radiotherapy must be evaluated for each woman on a case-by-case basis. Iatrogenic prematurity was common in our findings.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.277
Teacher spread0.272 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations14
Published2018
Admission routes1
Has abstractyes

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