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Record W4390169785 · doi:10.22374/cjgim.v18i4.706

Admissions for Presumed Social Reasons: Epidemiology, Risk Factors, and Hospital Outcomes

2023· article· en· W4390169785 on OpenAlexafffundvenue
Jasmine Mah, Samuel D. Searle, Katalin Koller, Gali Latariya, Karen Nicholls, Susan Freter, Maia von Maltzahn, Kenneth Rockwood, Melissa K. Andrew

Bibliographic record

VenueCanadian Journal of General Internal Medicine · 2023
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsNova Scotia Health AuthorityDalhousie University
FundersCanadian Institutes of Health ResearchConsortium canadien en neurodégénérescence associée au vieillissement
KeywordsMedicinePopulationHealth careHospital admission

Abstract

fetched live from OpenAlex

“Social admission” is a non-diagnostic label referring to an admission to a hospital for which no medical or health condition is deemed amenable to reversibility or rehabilitation; rather, the patient's social circumstances are felt to be the sole cause of hospitalization. There is a growing realization that medical facilities are experiencing an increase in socially vulnerable patient presentations. Clinicians also face challenges in caring for this patient population, which may have atypical presentations in which medical and social complexity often align. To better understand individuals admitted for social reasons and to guide future care and research, we review (i) the epidemiology, (ii) risk factors, and (iii) health outcomes associated with being labeled as “social admission.” We draw attention to factors that may improve care for this patient population and offer potential solutions with clinical relevance. Clinicians should remain mindful that patients labelled as “social admissions” often have complex underlying medical problems, which may be acute, and are at high risk of poor outcomes.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.165
GPT teacher head0.499
Teacher spread0.335 · 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

Citations6
Published2023
Admission routes3
Has abstractyes

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