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Record W2781604347 · doi:10.1002/pbc.26903

Recommendations for the treatment of children with radiotherapy in low‐ and middle‐income countries (LMIC): A position paper from the Pediatric Radiation Oncology Society (PROS‐LMIC) and Pediatric Oncology in Developing Countries (PODC) working groups of the International Society of Pediatric Oncology (SIOP)

2017· article· en· W2781604347 on OpenAlexaff
Jeannette Parkes, Clayton B. Hess, Hester Burger, Yavuz Anacak, Verity Ahern, Scott C. Howard, Moawia Mohammed Ali Elhassan, Soha Ahmed, Mithra Ghalibafian, Ahmed Nadeem Abbası, Bilal Mazhar Qureshi, Mohamed S. Zaghloul, Eduardo Zubizarreta, Pierre Bey, Alan Davidson, Éric Bouffet, Natia Esiashvili

Bibliographic record

VenuePediatric Blood & Cancer · 2017
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineRadiation therapyQuality assuranceMultidisciplinary approachPediatric oncologyDeveloping countryMedical physicsQuality (philosophy)ProcurementRadiation treatment planningProtocol (science)Quality of life (healthcare)Radiation oncologyIntensive care medicineNursingAlternative medicineCancerInternal medicineEconomic growth

Abstract

fetched live from OpenAlex

Pediatric radiotherapy is a critical part of pediatric oncology protocols and the quality of the radiotherapy may determine the future quality of life for long-term survivors. Multidisciplinary team decision making provides the basis for high-quality care. However, delivery of high-quality radiotherapy is dependent on resources. This article provides guidelines for delivery of good quality radiation therapy in resource-limited countries based on rational procurement and maintenance planning, protocol development, three-dimensional planning, quality assurance, and adequate staff numbers and training.

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)0.012
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.034
Threshold uncertainty score0.067

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0050.003
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0090.003

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 head0.020
GPT teacher head0.335
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations32
Published2017
Admission routes1
Has abstractyes

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