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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 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)0.003
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.036
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 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

Citations7
Published2019
Admission routes1
Has abstractyes

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