What do measures of ‘oral health‐related quality of life’ measure?
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
The terms 'health-related quality of life' and 'quality of life' are now in common use to describe the outcomes of oral health conditions and therapy for those conditions. In addition, there has been a proliferation of measures designed to quantify those outcomes. These measures, which were initially designated as socio-dental indicators or subjective oral health indicators are now more usually referred to as measures of oral health-related quality of life (OH-QoL). This is based on the assumption that the functional and psychosocial impacts they document must, of necessity, affect the quality of life. While this assumption has been subject to critical scrutiny in medicine, this is not the case with dentistry. Consequently, exactly what is being measured by indexes of OH-QoL is somewhat unclear. Based on the debate between Gill and Feinstein and Guyatt and Cook, we outline a number of criteria by means of which the construct addressed by measures of OH-QoL may be assessed. These are concerned with how the measures were developed and validated. These criteria are then used to appraise five of the many measures that have been developed over the past 20 years--the GOHAI, OHIP, OIDP, COHQoL and OH-QoL. The main conclusion is that while all document the frequency of the functional and psychosocial impacts that emanate from oral disorders they do not unequivocally establish the meaning and significance of those impacts. Consequently, the claim that oral disorders affect the quality of life has yet to be clearly demonstrated. Verifying this claim requires further qualitative studies of the outcomes of oral disorders as perceived by patients and persons, and the concurrent use of measures that more explicitly address the issue of quality of life.
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 | 0.020 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it