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Record W2160987690 · doi:10.3109/09593980802678422

Nonoperative management of a patient with a two-part minimally displaced proximal humerus fracture: A case report

2010· article· en· W2160987690 on OpenAlexaboutno aff
Patrick M Withrow, Judith Stoecker, Kelly Clark

Bibliographic record

VenuePhysiotherapy Theory and Practice · 2010
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRange of motionPhysical therapySling (weapon)RehabilitationPsychological interventionRandomized controlled trialConservative managementHumerusProximal humerusPhysical medicine and rehabilitationSurgeryNursing

Abstract

fetched live from OpenAlex

Proximal humerus fractures account for up to 10% of all fractures; however, the literature lacks any detailed nonoperative management protocols or treatment progression guidelines that are based on large randomized controlled trials. Several studies support conservative treatment of minimally displaced fractures, yet they do not document the specific interventions used nor do they provide rationale for progression. This case report describes the conservative rehabilitation of a patient following a traumatic, minimally displaced, two-part proximal humerus fracture. The patient was a 58-year-old female who was referred to physical therapy 4 weeks following arm sling immobilization with the primary goal of returning to full-time employment as a retail associate. Outcome measures included the McGill Pain Questionnaire, a numeric pain rating scale, and goniometric range of motion. Upon discharge the patient had zero complaints of pain, improved range of motion, and increased strength, and she returned to her prior level of function. The interventions used along with rationale are described in detail. The plan of care for this patient may be used as a framework for further research to determine the true effectiveness of nonoperative treatment.

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)0.001
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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.010
GPT teacher head0.347
Teacher spread0.337 · 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 designCase report
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

Citations2
Published2010
Admission routes1
Has abstractyes

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