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Health Care Resource Utilization after Acute Ankle Sprains

2012· article· en· W60447770 on OpenAlexaffabout
Iwona A. Bielska, William Pickett, Robert J. Brison, Brenda Brouwer, Ana Johnson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsQueen's University
Fundersnot available
KeywordsMedicineAnkleEmergency departmentWork productivityResource usePhysical therapyHealth careEmergency medicineAcute careProductivitySurgeryNursing

Abstract

fetched live from OpenAlex

Ankle sprains are common soft-tissue injuries that are often treated in emergency departments. These injuries can have significant consequences for the patient, including long-term morbidity and loss of productivity. The objective of this study was to examine the direct and indirect health resource utilization associated with ankle sprains. 296 adult patients with acute ankle sprains participated in the study in Kingston, Ontario, Canada. Data were collected using a one-month productivity questionnaire. Overall, 11% (95% CI, 8-15%) of the participants visited a physician following the initial emergency department visit. Almost all (95%; 95% CI, 92-97%) of the participants used medications or supportive treatments and 55% (95% CI, 50-61%) reported taking time off from work, school, or housework. The use of unpaid assistance was indicated by 56% (95% CI, 50-62%). Findings from this analysis highlight the significant patient-related and health care system burden of acute ankle sprains.

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)0.006
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 score0.276
Threshold uncertainty score0.550

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.033
GPT teacher head0.322
Teacher spread0.289 · 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

Citations1
Published2012
Admission routes2
Has abstractyes

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