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Record W4285365937 · doi:10.33962/roneuro-2021-053

Spinal conditions in geriatric patients in developing countries

2021· article· en· W4285365937 on OpenAlexaff
Abdulrahman Al-Mirza, Omar Al-Taei, Tariq Al‐Saadi

Bibliographic record

VenueRomanian Neurosurgery · 2021
Typearticle
Languageen
FieldMedicine
TopicSpine and Intervertebral Disc Pathology
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsMedicineNeurosurgerySpinal cord injurySpinal cordIntervention (counseling)SurgeryPediatrics

Abstract

fetched live from OpenAlex

Background: The spinal injury in an elderly patient is associated with higher mortality and an increased frequency of life-threatening complications and specifically spinal cord injuries. The aim of this study is to study the prevalence of geriatric spinal neurosurgical conditions in the Neurosurgical Department at Khoula Hospital, Muscat, Sultanate of Oman. Results: 171 patients were admitted due to spinal pathologies, which will be the main focus of the present study with an average age of 70.7 years. The male-to-female ratio was (1.5:1). Degenerative conditions were the most common spinal diagnosis (90.6%) followed by traumatic accidents (2.9%). Most of the patients underwent surgical intervention (78.9%). The majority of the patients (91.2%) of the patients stayed in the hospital for less than 15 days. There was a significant difference between the age of patients above and below 75 years the gender (p=0.003) and between the length of stay and type of intervention (P<0.005). Conclusion: Spinal cord-related pathologies are a growing cause of mortality and morbidity worldwide, because of the increasing number of elderly people due to an increasingly rising life span worldwide. In the present study, degenerative conditions were the most common spinal diagnosis followed by traumatic accidents.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
GPT teacher head0.282
Teacher spread0.262 · 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
Published2021
Admission routes1
Has abstractyes

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