Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Women perceive, describe and react to pain differently; it cannot be easily quantified. Pain is a private and personal experience to the individual. It is therefore impossible for another person to know exactly what someone else's pain feels like. When measuring pain there is a need to assess both the intensity and the quality of the pain to gain an insight into a person's experience of pain. During a PhD Study, which involved the investigation of the effectiveness of localised cooling treatments to alleviate perineal pain, women were asked to describe the pain as well as the intensity (Steen and Marchant, 2007). The quality of pain was measured by asking the woman to describe the pain in her own words. These words were analyzed as pain descriptors under the themes of sensory, affective, evaluative and miscellaneous as described by the McGill Pain Scale. In addition, intensity, discomfort, physical symptoms, metaphors used and links to the expectations of the woman were considered (Melzack and Wall, 1996). This article will give an overview of the pain syndrome, discuss measurement of pain methods and the use of word descriptors to assess the quality of pain. The assessment of perineal pain and women's descriptions will be further explored. This insight will give an understanding of the pain experience of women who have perineal trauma following childbirth and this may lead to further research and the development of a specific evaluating tool.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 it