Cmtre; Irutitutz for Clinical Eaaluatiue Sciences (L.E.E); and Princess Margaret Hospital
Bibliographic record
Abstract
fusearch indi,cates that patientsfeel more satisf,ed and obtain better health outcomcs when thq are able to discuss tkcir Etcstions wi,th their hcalth professionals. A better understanding of cancer patirnts ' questions may lnlp guid"e intertentions to address tfuir information nuds and improae pain managetnent. This study sought to explore and fuscribe the questions that women with breast cancer haae about pai,n related to cancen Semistructtned interaiat)s were conducted wi,th womcn with pain related to breast cancer m its treatrnent, reruitedfrom a large teaching hospital in Tmonto, Canada. Interaiews were audio recordcd and fully transcribed. Data saturation was reached after 18 parti,cipants wne i.nterviaued. Analysis inaolaed thp identffication of themes and tfu dnelopment of a taxonomy of questions about pain. In total, oaer 200 questions concerning seaen main themes une identif,ed: (1) und.erstand,i.ng cancer pain, (2) hnowing what t0 expect, (3) options for pai.n control, (4) coping wi,th cancer pain,(5) talking uith others with cancer pain, (6) f,nding help managing cancer pain, and (7) describing pain. The infarmation collected, suggests that fornulating and articulnting questions about pain is a context-d,ependent, time-intensi.ae process that requires reflection, hnowlzdge, and a good use of language. Pati.ents haue numtrous and d,iunse questions about
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.420 | 0.098 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".