Descriptions of pain in elderly patients following orthopaedic surgery
Bibliographic record
Abstract
The aims of this study were to investigate what words elderly patients, who had undergone hip surgery, used to describe their experience of pain in spoken language and to compare these words with those used in the Short-Form McGill Pain Questionnaire (SF-MPQ) and Pain-O-Meter (POM). The study was carried out at two orthopaedic and two geriatric clinical departments at a large university hospital in Sweden. Altogether, 60 patients (mean age =77) who had undergone orthopaedic surgery took part in the study. A face-to-face interview was conducted with each patient on the second day after the operation. This was divided into two parts, one tape-recorded and semi-structured in character and one structured interview. The results show that a majority of the elderly patients who participated in this study verbally stated pain and spontaneously used a majority of the words found in the SF-MPQ and in the POM. The patients also used a number of additional words not found in the SF-MPQ or the POM. Among those patients who did not use any of the words in the SF-MPQ and the POM, the use of the three additional words 'stel' (stiff), 'hemsk' (awful) and 'rad(d)(sla)' (afraid/fear) were especially marked. The patients also combined the words with a negation to describe what pain was not. To achieve a more balanced and nuanced description of the patient's pain and to make it easier for the patients to talk about their pain, there is a need for access to a set of predefined words that describe pain from a more multidimensional perspective than just intensity. If the elderly patient is allowed, and finds it necessary, to use his/her own words to describe what pain is but also to describe what pain is not, by combining the words with a negation, then the risk of the patient being forced to choose words that do not fully correspond to their pain can be reduced. If so, pain scales such as the SF-MPQ and the POM can create a communicative bridge between the elderly patient and health care professionals in the pain evaluation process.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".