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Record W3008086335 · doi:10.1016/s2589-7500(20)30020-0

3D technology and telemedicine in humanitarian settings

2020· article· en· W3008086335 on OpenAlexaboutno aff
Pierre Moreau, Samar Ismael, Hatim Masadeh, Esraa Al Katib, Laetitia Viaud, Clara Nordon, Safa Herfat

Bibliographic record

VenueThe Lancet Digital Health · 2020
Typearticle
Languageen
FieldMedicine
TopicBurn Injury Management and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRehabilitationPsychosocialTelemedicineScopusHealth careMEDLINEMedical emergencyPhysical therapy

Abstract

fetched live from OpenAlex

Worldwide, sizeable populations are living with disabling and disfiguring injuries and are unable to access rehabilitation services.1,2 In 2017, the Médecins Sans Frontières (MSF) Foundation initiated a pilot project with the MSF Reconstructive Surgery Program (RSP; established in 2006) in Amman, Jordan, to help to provide comprehensive rehabilitation services for patients with facial burns and upper limb differences. A multidisciplinary team, spanning specialists with expertise in prosthetics and orthotics, physical and occupational therapy, rehabilitation medicine, surgery, and biomedical engineering, collaborated on development of personalised prosthetic and orthotic devices using 3D technologies and telemedicine (appendix).

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)0.007
metaresearch head score (Gemma)0.033
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: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.018
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.033
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0070.008
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0180.001

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 head0.035
GPT teacher head0.311
Teacher spread0.276 · 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
GenreReview

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

Citations8
Published2020
Admission routes1
Has abstractyes

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