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Record W2930294435 · doi:10.21873/anticanres.13306

Satisfaction After Joint-preservation Surgery in Patients With Musculoskeletal Knee Sarcoma Based on Various Scores

2019· article· en· W2930294435 on OpenAlexaboutno aff
Kensaku Abe, Norio Yamamoto, Katsuhiro Hayashi, Akihiko Takeuchi, Satoshi Kato, Shinji Miwa, Kentaro Igarashi, Hiroyuki Inatani, Yu Aoki, Takashi Higuchi, Yuta Taniguchi, Hiroyuki Tsuchiya

Bibliographic record

VenueAnticancer Research · 2019
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineKnee JointPhysical therapyJoint (building)Joint replacementPatient satisfactionSarcomaSurgeryArthroplastyPathology

Abstract

fetched live from OpenAlex

BACKGROUND/AIM: At our institute, we prioritize joint-preservation whenever possible in cases of musculoskeletal knee sarcoma. This study aimed to evaluate patient satisfaction after joint-preservation surgery using different scales. PATIENTS AND METHODS: Surveys were mailed to 62 patients with musculoskeletal knee sarcoma. We analyzed the responders' data based on the Musculoskeletal Tumor Society (MSTS) score, Toronto Extremity Salvage Score (TESS), and three component scores (physical, mental, and role/social) of the 36-Item Short-Form Health Survey according to whether they belonged to patients in the joint-preservation or in the joint-replacement groups. RESULTS: The survey response rate was 67.7%. MSTS and TESS scores were higher in the patients in the joint-preservation group than in the joint-replacement group, although the differences lacked statistical significance. CONCLUSION: Better physical outcomes improve patient satisfaction, as demonstrated by the high satisfaction in the group with joint-preservation.

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 score0.016
Threshold uncertainty score0.777

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.036
GPT teacher head0.325
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 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

Citations7
Published2019
Admission routes1
Has abstractyes

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