Bibliographic record
Abstract
Despite fatigue being one of the most common symptoms presenting in primary care, clinicians and researchers were constrained by a lack of validated, reliable and fatigue-specific measures. In 1994, a Canadian group frustrated by this limitation developed the Fatigue Impact Scale (FIS) [1]. Since then, it remains one of the most widely used tools, although there now exist modified versions [the modified Fatigue Impact Scale (MFIS), the daily FIS, the unidimentional FIS and the abbreviated MFIS]. The FIS has been translated and validated in 30 languages. It is a detailed and relatively lengthy tool, which takes ∼3 min to complete in a non-fatigued person, but may take much longer in a severely fatigued respondent. The subject completes the tool personally, rather than having an interview and thus, no training is required to deliver it. Scoring is simple and is described briefly below (further details can be found from reference [2]). The score reflects functional limitation due to fatigue experienced within the previous month rather than a measure of the level of fatigue. It may be used in both the clinical and the research setting in people for whom fatigue is a predominant symptom.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.006 |
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".