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Record W1597938904 · doi:10.1111/vru.12161

A SURVEY OF VETERINARY RADIATION FACILITIES IN 2010

2014· article· en· W1597938904 on OpenAlexaboutno aff
John Farrelly, Margaret C. McEntee

Bibliographic record

VenueVeterinary Radiology & Ultrasound · 2014
Typearticle
Languageen
FieldMedicine
TopicVeterinary Oncology Research
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRadiation oncologyPrivate practiceBasal cellVeterinary medicineMedical physicsRadiation therapyFamily medicineRadiologyInternal medicine

Abstract

fetched live from OpenAlex

A survey of veterinary radiation therapy facilities in the United States, Canada, and Europe was done in 2010, using an online survey tool, to determine the type of equipment available, radiation protocols used, caseload, tumor types irradiated, as well as other details of the practice of veterinary radiation oncology. The results of this survey were compared to a similar survey performed in 2001. A total of 76 facilities were identified including 24 (32%) academic institutions and 52 (68%) private practice external beam radiation therapy facilities. The overall response rate was 51% (39/76 responded). Based on this survey, there is substantial variation among facilities in all aspects ranging from equipment and personnel to radiation protocols and caseloads. American College of Veterinary Radiology boarded radiation oncologists direct 90% of the radiation facilities, which was increased slightly compared to 2001. All facilities surveyed in 2010 had a linear accelerator. More facilities reported having electron capability (79%) compared to the 2001 survey. Eight facilities had a radiation oncology resident, and academic facilities were more likely to have residents. Patient caseload information was available from 28 sites (37% of radiation facilities), and based on the responses 1376 dogs and 352 cats were irradiated in 2010. The most frequently irradiated tumors were soft tissue sarcomas in dogs, and oral squamous cell carcinoma in cats.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.081
GPT teacher head0.355
Teacher spread0.274 · 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 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

Citations38
Published2014
Admission routes1
Has abstractyes

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