Older African-Americans with osteoarthritis of the knee preferred to avoid total knee replacement surgery
Bibliographic record
Abstract
Figaro MK, Russo PW, Allegrante JP. Preferences for arthritis care among urban African Americans: “I don’t want to be cut.” Health Psychol 2004;23:324–9.[OpenUrl][1][CrossRef][2][PubMed][3][Web of Science][4] Q What are the preferences and expectations of older urban African-Americans regarding total knee replacement (TKR) for osteoarthritis (OA) of the knee? Qualitative study based on the theory of reasoned action as a model of behaviour. Communities in northern Manhattan, New York, USA. 94 African-Americans >50 years of age (mean age 71 y, 84% women) with medical insurance, who had pain or stiffness in one or both knees that made walking difficult or slow during the previous 6 months, and who lived or attended church or a senior centre in Harlem. 13% of patients had had TKR. Data were collected during 45–75 minute structured face to face or telephone interviews that included both closed and open ended questions. Responses to open ended questions were recorded and transcribed verbatim. Major themes were developed through a process of categorisation. Preference for natural remedies . 36% of patients thought that OA was caused by cold or dampness, either to the joint or from the environment. They tended to think that OA was a natural, irremediable, inevitable deterioration and a sign of ageing. A strong trend toward … [1]: {openurl}?query=rft.jtitle%253DHealth%2Bpsychology%2B%253A%2B%2Bofficial%2Bjournal%2Bof%2Bthe%2BDivision%2Bof%2BHealth%2BPsychology%252C%2BAmerican%2BPsychological%2BAssociation%26rft.stitle%253DHealth%2BPsychol%26rft.aulast%253DFigaro%26rft.auinit1%253DM.%2BK.%26rft.volume%253D23%26rft.issue%253D3%26rft.spage%253D324%26rft.epage%253D329%26rft.atitle%253DPreferences%2Bfor%2Barthritis%2Bcare%2Bamong%2Burban%2BAfrican%2BAmericans%253A%2B%2526quot%253BI%2Bdon%2527t%2Bwant%2Bto%2Bbe%2Bcut%2526quot%253B.%26rft_id%253Dinfo%253Adoi%252F10.1037%252F0278-6133.23.3.324%26rft_id%253Dinfo%253Apmid%252F15099175%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=10.1037/0278-6133.23.3.324&link_type=DOI [3]: /lookup/external-ref?access_num=15099175&link_type=MED&atom=%2Febnurs%2F8%2F1%2F32.1.atom [4]: /lookup/external-ref?access_num=000221120800014&link_type=ISI
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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; a candidate call from one teacher head, not a consensus.
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".