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Record W2553387669 · doi:10.1120/jacmp.v17i6.6553

Implementation of quality medical physics training in a low‐middle income country — sharing experience from a tertiary care JCIA‐accredited university hospital

2016· article· en· W2553387669 on OpenAlexaboutno aff
Ahmed Nadeem Abbası, Wazir Muhammad, Amjad Hussain

Bibliographic record

VenueJournal of Applied Clinical Medical Physics · 2016
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsnot available
Fundersnot available
KeywordsAccreditationRadiation oncologyQuality assuranceMedicineMedical educationHealth careCertificationMedical physicistSpecialtyQuality (philosophy)Medical physicsFamily medicinePolitical scienceRadiation therapySurgeryPhysics

Abstract

fetched live from OpenAlex

MENTORS AND FACILITATORSThere are six qualified medical physicists, a radiation safety officer, and four radiation oncologists available for teaching in this structured program. JOB OPPORTUNITIESKarachi is the largest city in the country and is a home to several private sector hospitals that offer radiotherapy services.A large number of cancer patients is seen to in these public sector medical centers.Eighteen of these centers operate under the umbrella of the PAEC (Pakistan Atomic Energy Commission).Theses medical centers, however, hire medical physics graduates only from PIEAS (Pakistan Institute of Engineering and Applied sciences).In the private sector, several medical centers have been established with the provision of standard care services available to the cancer patients.

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)0.008
metaresearch head score (Gemma)0.035
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.035
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0040.002
Scholarly communication0.0040.003
Open science0.0020.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0070.001

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 head0.039
GPT teacher head0.428
Teacher spread0.389 · 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
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 routes1
Has abstractyes

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