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Record W2804298079 · doi:10.29315/gm.v3i1.5

Five Years of Humanitarian Missions in São Tomé and Príncipe

2016· article· en· W2804298079 on OpenAlexaff
Cristina Caroça, João Paço

Bibliographic record

VenueGazeta Médica · 2016
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsOtorhinolaryngologyHealth careTonsillectomyMedicineWork (physics)Health professionalsUnit (ring theory)Medical emergencyMedical educationAudiologyPsychologySurgeryEngineeringPolitical science

Abstract

fetched live from OpenAlex

INTRODUCTION: Since February 2011, a group of otolaryngologists from CUF Infante Santo Hospital, a private healthcare unit in Portugal, invited by a non-governmental organization to provide equipment and properly skilled professionals to help and treat otolaryngology diseases in São Tomé and Príncipe. These missions included surgical procedures, consultation and hearing evaluation.
 METHODS: This work is a retrospective chart review of all otolaryngology cases performed during these missions since 2011 to 2016, and what we done during mission.
 RESULTS: During these missions, we have found some common pathologies. Deafness is the most prevalent after which follows the lymphoid tissue of oropharynx pathology. On these 18 missions a total of 1057 otolaryngology assessments were conducted. The main surgery was oral cavity with adenoidectomy and tonsillectomy. The results of all audiological tests performed during these 18 missions, reveal an increase of sensorineural deafness.
 DISCUSSION: These missions’ purpose is to allow healthcare access to all, to identify people with hearing and language problems and to adapt prosthetics, if possible, mainly for children and young adults.We have witnessed a considerable improvement on the children to whom we have adapted prosthetics. Some of them return to school, have friends and became more social. As the result of this work, we conclude that all Humanitarian Missions must be adapted to each country’s needs as we have done over the past five 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.507
Threshold uncertainty score0.245

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.290
Teacher spread0.272 · 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 teacher head, 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
Published2016
Admission routes1
Has abstractyes

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