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Record W1985970017 · doi:10.1097/spc.0b013e32834903c3

Malignant extradural spinal cord compression in men with prostate cancer

2011· review· en· W1985970017 on OpenAlexaff
Andrew Loblaw, Gunita Mitera

Bibliographic record

VenueCurrent Opinion in Supportive and Palliative Care · 2011
Typereview
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsHealth Sciences CentreCanada Auto WorkersSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineSpinal cord compressionProstate cancerSpinal cordProstateSpinal Cord NeoplasmCancerInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Malignant epidural spinal cord compression (MESCC) is a dreaded complication of malignancy and is fortunately not common. Approximately 7% of men dying of prostate cancer will have at least one episode of MESCC during their lifetime. Treatment needs to be individualized and estimating the prognosis is critical to achieving a balance between effectiveness therapy and the burden of treatment. RECENT FINDINGS: A consortium of multiple centers has defined prognosis scales, and multiple randomized studies have helped define the optimal dose fractionation schedule for patients getting radiotherapy. SUMMARY: Simple prognosis scales available to assist the clinician are reviewed. For poor prognosis patients, a single fraction of 8 Gy is just as effective as multiple fractions, however, are much more convenient. For good prognosis patients, surgery and radiation should be considered. For patients not getting surgery, enrollment in clinical trials of single vs. multiple fractions of radiation should be a priority. For high-risk patients, screening strategies are being developed and hold promise for maintaining ambulation throughout the patients' lifetime.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.206
GPT teacher head0.460
Teacher spread0.254 · 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 designNot applicable
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

Citations6
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Opinion in Supportive and Palliative CareSame topicManagement of metastatic bone diseaseFrench-language works237,207