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Record W4392257721 · doi:10.53555/sfs.v10i5.2210

The Role Of Radiology And Physical Therapy In Diagnosing And Treating Fractures In Traffic Accident Patients

2023· article· en· W4392257721 on OpenAlexvenueno aff
Hanan. M. Alfaifi, Abeer. G. Alharbi, Layla. I. Maes, Khalid Mobarak K Alharthi, Mohammad. A. Arif, Abdulrahman. I. Abbas, Ahmad. H. Nashily, Ismail. K. Alshabi, Taha. M. Alhudali, Abdullah. N. Alotaiby, Hattan. O. Alsalmi, Raed. S. Almalki, Abdulrhman. M. Alharbi, Mansour Alzahrani, Hadeel. I. Al-Deeb, Yahya. Q. Sharahili, Mohanna. R. Al-roog, Fahad. M. Alzahrani, Abduaziz. M Alalwani, Hanan O Almandeel, Ahmad M Alawi, Adulaziz. S Alghamdi

Bibliographic record

VenueJournal of Survey in Fisheries Sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsnot available
FundersUmm Al-Qura University
KeywordsMedicineAccident (philosophy)Medical physicsRadiologyIntensive care medicine

Abstract

fetched live from OpenAlex

the aim of the current study is, What are the types of fractures that affect humans in traffic accidents, what is the role of x-rays in diagnosing fractures for patients, treatment methods for those with fractures in traffic accidents., The questionnaire was created electronically via the Google Drive program, and then it was distributed via mobile phone on the social networking program (WhatsApp). Using e-mail for all participants to respond to the questionnaire. 600 questionnaires were distributed to all mobile groups, and 550 questionnaires were received on the researcher’s e-mail. (The target group is residents of the Holy City of Mecca, aged 25-60 years).

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)0.002
metaresearch head score (Gemma)0.016
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 score0.007
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.081
GPT teacher head0.323
Teacher spread0.241 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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