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Record W2509708170 · doi:10.1118/1.4961853

Sci‐Fri PM: Radiation Therapy, Planning, Imaging, and Special Techniques ‐ 10: Results from Canada Wide Survey on Total Body Irradiation Practice

2016· article· en· W2509708170 on OpenAlexaffabout
Ryan Studinski, D Fraser, Rajiv Samant, Miller MacPherson

Bibliographic record

VenueMedical Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsStaffingMedical prescriptionMedicineMedical physicsTotal body irradiationVariety (cybernetics)Nuclear medicineFamily medicineComputer scienceSurgeryNursing

Abstract

fetched live from OpenAlex

Purpose: Total Body Irradiation (TBI) is delivered to a relatively small number of patients with a variety of techniques; it has been a challenge to develop consensus studies for best practice. This survey was created to assess the current state of TBI in Canada. Methods: The survey was created with questions focusing on the radiation prescription, delivery technique and resources involved. The survey was circulated electronically to the heads of every clinical medical physics department in Canada. Responses were gathered and collated, and centres that were known to deliver TBI were urged to respond. Results: Responses from 20 centres were received, including 12 from centres that perform TBI. Although a variety of TBI dose prescriptions were reported, 12 Gy in 6 fractions was used in 11 centres while 5 centres use unique prescriptions. For dose rate, a range of 9 to 51 cGy/min was reported. Most centres use an extended SSD technique, with the patient standing or lying down against a wall. The rest use either a “sweeping” technique or a more complicated multi‐field technique. All centres but one indicated that they shield the lungs, and only a minority shield other organs. The survey also showed that considerable resources are used for TBI including extra staffing, extended planning and treatment times and the use of locally developed hardware or software. Conclusions: This survey highlights that both similarities and important discrepancies exist between TBI techniques across the country, and is an opportunity to prompt more collaboration between centres.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.030
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.010
GPT teacher head0.284
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 source (direct Gemma or distilled Codex), 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

Citations5
Published2016
Admission routes2
Has abstractyes

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