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Record W2048903547 · doi:10.1089/jpm.2006.9.850

Mild, Moderate, or Severe Pain Categorized by Patients with Cancer with Bone Metastases

2006· article· en· W2048903547 on OpenAlexaff
Edward Chow, Meagan Doyle, Kathy Li, Nicole Bradley, Kristin Harris, George Hruby, Emily Sinclair, Elizabeth Barnes, Cyril Danjoux

Bibliographic record

VenueJournal of Palliative Medicine · 2006
Typearticle
Languageen
FieldMedicine
TopicManagement of metastatic bone disease
Canadian institutionsSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicinePalliative careBreast cancerPain assessmentBrief Pain InventoryPhysical therapyCancerInternal medicineSurgeryPain managementChronic pain

Abstract

fetched live from OpenAlex

PURPOSE: To examine how patients categorize their pain with the two commonly employed scales. METHODS AND MATERIALS: Patients with bone metastases referred to an outpatient palliative radiotherapy clinic were asked to rate their current pain on a numerical scale of 0-10 (0 = no pain, 10 = worst pain possible) and a categorized scale: none, mild, moderate and severe. RESULTS: Two hundred and seventeen patients were enrolled in the study. The median age was 66 years and median Karnofsky Performance Score was 70. The most common primary cancer sites were lung, prostate and breast. Based on patient-evaluated symptoms, 60% of patients who categorized pain as mild assigned it a 3 (24%) or 4 (36%), 63% who categorized pain as moderate assigned it a 5 (16.9%), 6 (19.1%) or 7 (27%) and 80% who categorized pain as severe assigned it an 8 (28.2%), 9 (12.6%) or 10 (39.8%). CONCLUSION: Our patients scored pain as mild if pain was < or =4, moderate if pain was 5-7 and severe if pain was > or =8.

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)0.004
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.292
Teacher spread0.271 · 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

Citations46
Published2006
Admission routes1
Has abstractyes

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