Bibliographic record
Abstract
Antimicrobial therapy of infectious diseases has become complex, even to the most pragmatic clinical practitioner. There are increasing numbers and classes of antibiotics and adjunctive therapies, increasingly specific clinical and microbiological diagnoses of infectious diseases, and an increasingly aged, immune-compromised and debilitated patient population, with chronic concurrent diseases and complex care requirements. On top of that is a proliferation of increasingly extensive, complex and changing treatment guidelines, which are scattered across professional journals and websites, and are usually confined in content to a clinical disease syndrome or to a specific infection at a subspecialty level. As a consultant specialist physician practicing in the field, I see concise tabular therapeutic manuals used to inform diagnosis and diagnostic manuals to instruct on treatment. Manuals of medical management abound, are usually instructive if not explanatory and have sometimes outgrown the marsupial capacity of their users. Manuals are appearing on PDAs and handheld computers, which might accommodate tome-like quantities of information for viewing through the keyhole of a three-inch digital display. Brought to us by a clinical microbiologist, STAT is a speedy summary guide to key medical information on common infectious diseases. STAT is a reliably low-tech wire-bound lab-coat-pocket-sized clinical manual that guides the treating physician in a concise, explanatory and stepwise manner on antimicrobial therapy of 15 syndromes – from head to toe, from sinusitis to meningitis, and from cystitis to diabetic foot infection. There is a current guidelines-referenced key information display, in one-page columns and figures, or two-page tables. There is explanatory diagnostic, management and treatment information, along with pros and cons of alternatives. Points on evaluation of response and convalescent management take one further into practice problems than the usual tabulation of diagnoses and treatments. For the interested, there is an introductory section on antimicrobial resistance. For the engaged, there is a section on the pharmacodynamic concept of the drug concentration dependence of mutational resistance selection and its prevention in therapy. For those who especially enjoy numeracy, a closing section offers a formulaic approach to deriving empiric treatment choices from etiological and drug-resistant fractions of pathogens. For instance, from the etiologic fractions of five pathogens of community-acquired pneumonia, and the fractions of each with resistance to a candidate antibiotic treatment, the sum probabilities of appropriateness of antibiotic treatment options for the syndrome are numerically ranked. For those communities and hospitals with diagnostic and susceptibility surveillance, this allows rational treatment guidelines to be informed by local data. Where resistance is changing in time and by locale, and as it drives choice and success of treatments, this is a simple explanatory contribution to local patient care and antibiotic stewardship. It was a pleasure to see the intention of the author to teach the user in the STAT manual. The novel contribution is the conceptual background on antibiotic resistance and explanatory practical treatment guidelines in an accessible pocket reference. It is a welcome addition to the pockets of our doctors and workbenches of our clinics.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.515 | 0.424 |
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".