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RADIOTHERAPY AND SURGERY FOR FELINE SOFT TISSUE SARCOMA

2009· article· en· W1997523988 on OpenAlexaff
Monique N Mayer, Philip L. Treuil, Susan Μ. LaRue

Bibliographic record

VenueVeterinary Radiology & Ultrasound · 2009
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineCATSRadiation therapySoft tissueSurgerySoft tissue sarcomaAnemiaSurvival rateMedical recordInternal medicine

Abstract

fetched live from OpenAlex

Medical records for 79 cats with soft tissue sarcomas treated with preoperative or postoperative curative intent radiation therapy between August 1994 and February 2004 were reviewed. The purpose was to assess the effectiveness of preoperative and postoperative radiation therapy, and to determine the association of patient and radiation treatment variables with survival. Gender, age, weight, anatomic tumor site, packed cell volume (PCV), computerized vs. manual treatment planning, radiation field length, preoperative vs. postoperative irradiation, total radiation dose, and biologically effective dose (BED) were assessed as prognostic factors for survival. Fifty-six of 79 (71%) of cats were anemic within 2 weeks before or during radiation treatment. The median survival was 520 days for all cats, with a 1-year survival rate of 61.6%, and a 2-year survival rate of 41.6%. Only timing of radiation therapy relative to surgery and presence of a moderate or severe anemia were significantly related to survival. The median survival was 310 days for cats treated with preoperative radiation therapy, and 705 days for cats treated with postoperative radiation therapy (P = 0.03). The median survival was 308 days for cats with a PCV<25%, and 760 days for cats with a PCV > or = 25% (P = 0.017). Radiation therapy in combination with surgery results in relatively long-term survival in cats with soft tissue sarcomas. Anemia is common in cats undergoing radiation therapy for soft tissue sarcomas, and is associated with decreased survival.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.911
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.054
GPT teacher head0.369
Teacher spread0.315 · 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.

Study designBench or experimental
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

Citations30
Published2009
Admission routes1
Has abstractyes

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