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Record W2472929824 · doi:10.1001/jamasurg.2016.1297

Results of a Nationwide Capacity Survey of Hospitals Providing Trauma Care in War-Affected Syria

2016· article· en· W2472929824 on OpenAlexaff
Hani Mowafi, Mahmoud Hariri, Houssam Alnahhas, Elizabeth Ludwig, Tammam Allodami, Bahaa Mahameed, Jamal Kaby Koly, Ahmed Aldbis, Maher Saqqur, Baobao Zhang, Anas Al-Kassem

Bibliographic record

VenueJAMA Surgery · 2016
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicinePharmacyPopulationSalaryHealth careAbbreviated Injury ScaleMedical emergencyEmergency medicineFamily medicineInjury Severity ScoreInjury preventionPoison controlEnvironmental health

Abstract

fetched live from OpenAlex

IMPORTANCE: The Syrian civil war has resulted in large-scale devastation of Syria's health infrastructure along with widespread injuries and death from trauma. The capacity of Syrian trauma hospitals is not well characterized. Data are needed to allocate resources for trauma care to the population remaining in Syria. OBJECTIVE: To identify the number of trauma hospitals operating in Syria and to delineate their capacities. DESIGN, SETTING, AND PARTICIPANTS: From February 1 to March 31, 2015, a nationwide survey of 94 trauma hospitals was conducted inside Syria, representing a coverage rate of 69% to 93% of reported hospitals in nongovernment controlled areas. MAIN OUTCOMES: Identification and geocoding of trauma and essential surgical services in Syria. RESULTS: Although 86 hospitals (91%) reported capacity to perform emergency surgery, 1 in 6 hospitals (16%) reported having no inpatient ward for patients after surgery. Sixty-three hospitals (70%) could transfuse whole blood but only 7 (7.4%) could separate and bank blood products. Seventy-one hospitals (76%) had any pharmacy services. Only 10 (11%) could provide renal replacement therapy, and only 18 (20%) provided any form of rehabilitative services. Syrian hospitals are isolated, with 24 (26%) relying on smuggling routes to refer patients to other hospitals and 47 hospitals (50%) reporting domestic supply lines that were never open or open less than daily. There were 538 surgeons, 378 physicians, and 1444 nurses identified in this survey, yielding a nurse to physician ratio of 1.8:1. Only 74 hospitals (79%) reported any salary support for staff, and 84 (89%) reported material support. There is an unmet need for biomedical engineering support in Syrian trauma hospitals, with 12 fixed x-ray machines (23%), 11 portable x-ray machines (13%), 13 computed tomographic scanners (22%), 21 adult (21%) and 5 pediatric (19%) ventilators, 14 anesthesia machines (10%), and 116 oxygen cylinders (15%) not functional. No functioning computed tomographic scanners remain in Aleppo, and 95 oxygen cylinders (42%) in rural Damascus are not functioning despite the high density of hospitals and patients in both provinces. CONCLUSIONS AND RELEVANCE: Syrian trauma hospitals operate in the Syrian civil war under severe material and human resource constraints. Attention must be paid to providing biomedical engineering support and to directing resources to currently unsupported and geographically isolated critical access surgical hospitals.

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)0.004
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.107
GPT teacher head0.384
Teacher spread0.277 · 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.

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

Citations31
Published2016
Admission routes1
Has abstractyes

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