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Record W3182415830 · doi:10.1111/ans.17028

The need for improved patient reported outcome measures in patients with extremity sarcoma: a narrative review

2021· review· en· W3182415830 on OpenAlexaboutno aff
Thomas J. Blight, Peter Choong

Bibliographic record

VenueANZ Journal of Surgery · 2021
Typereview
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePromSarcomaPatient-reported outcomeQuality of life (healthcare)Physical therapyPathologyNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Extremity sarcoma causes impairments to functionality and quality of life. Patient-reported outcome measures (PROMS) assess patient perspectives relating to domains of health and quality of life. METHODS: To describe PROMs utilised in extremity sarcoma, the available literature was screened for studies that utilised PROMs to evaluate outcomes in extremity sarcoma following surgery. RESULTS: Seventy articles met eligibility criteria; six PROMs were identified. The Toronto Extremity Salvage Score, The Short-Form 36, The EORTC QLQ-C30, The Disabilities of the Arm, Shoulder and Hand questionnaire, the Reintegration to Normal Living index and the Patient-Reported Outcomes Measurement Information System. Most sarcoma patients score well in these tools, with bone sarcoma, and extent of resection being predictors of poor outcomes. CONCLUSION: TESS is the only sarcoma-specific PROM, and though a valid assessment of functionality, it has difficulty differentiating patients with minor functional impairments. The absence of a disease-specific measure of health is concerning, as generic tools do not account for the unique experiences sarcoma patients face and may impair their accuracy in analysing intervention effectiveness.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.900
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.126
GPT teacher head0.353
Teacher spread0.227 · 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 designOther design
Domainnot available
GenreReview

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

Citations10
Published2021
Admission routes1
Has abstractyes

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